{ "title": "Photo calorie counting accuracy: source registry", "publisher": "Calk, https://calk.me", "article": "https://calk.me/articles/photo-calorie-counting-accuracy/", "reviewed": "2026-09-05", "coverage": "Published 2015 to 5 September 2026: peer-reviewed studies, preprints, conference abstracts, editorial and personal tests, developer claims, press. Searched in English, German, French; catalogue also in Russian, Spanish, Portuguese.", "use": "Free to reuse and quote with attribution to Calk and a link to the article. Numbers were read from the original sources; recomputed values carry their formula.", "status_values": { "charted": "one of the 18 rows on the article chart", "registered_not_charted": "verified record with no usable calorie-error percentage, or with a number we could not defend on the chart (see reason)", "catalogued_not_charted": "source in the article's extended catalogue; no chart point (see reason)", "rejected": "candidate that failed verification (see reason)" }, "counts": { "charted": 18, "registered_not_charted": 50, "catalogued_not_charted": 21, "rejected": 75 }, "columns": { "record_id": "stable id; SCI-* science, app1-*/app2-* developer claims, test-* editorial and personal tests, cat-* catalogue-only sources, rej-* rejected candidates", "status": "charted | registered_not_charted | catalogued_not_charted | rejected", "reason": "why the record is on the chart, why it is not, or why it was rejected", "group": "A = scientific study or equally transparent independent check; B = personal experiment, editorial review, developer claim, press", "independent": "yes/no/unclear: whether the developer of the tested product took part in the measurement", "chart_*": "what the article chart shows for this record (18 rows); chart_is_lower_bound = the figure is a minimum derived from mean bias", "registry_*": "the number as first extracted by the research pass, before editorial recomputation", "original_metric / original_numbers / quote_or_location": "what the source itself reports and where", "recomputation_formula / assumptions": "how a chart figure was derived when it is not printed in the source", "commercial_ties / ties_note": "funding and author affiliations as disclosed" }, "rows": [ { "record_id": "SCI-01-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "Herzig · SNAQ", "chart_metric": "Average error per meal", "chart_low_percent": 12.7, "chart_high_percent": 12.7, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "12.7% average error per meal (MAPE), 41.2 kcal on average. Depth-sensing iPhone, food chosen from a predefined list, segmentation could be corrected by hand.", "chart_limit": "Academic study; the authors thank SNAQ's developers and declare no conflicts. This is assisted logging, not a bare photo.", "title": "Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study", "product": "SNAQ app (SNAQ GmbH, Zurich), software version May 2018, iPhone X with structured-light depth sensor", "product_version": "app software version May 2018", "measured_by": "Herzig D, Nakas CT, Stalder J, Kosinski C, Laesser C, Dehais J, Jaeggi R, Leichtle AB, Dahlweid FM, Stettler C, Bally L (academic team)", "author_org": "Bern University Hospital / Inselspital, University of Bern; University of Thessaly", "publisher": "JMIR mHealth and uHealth 2020;8(3):e15294", "language": "en", "pub_date": "2020-03-25", "test_date": "not stated in article (app version May 2018; study conducted before publication)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7142738/", "archive_url": "", "sample_size": "48 test meals / 128 food items", "unit": "meal", "reference_and_conditions": "Every food item weighed to the nearest 0.1 g on a precision scale (Mettler Toledo ME4002); macronutrient/energy from composition tables. Standardised capture in a hospital kitchen, natural light, randomised viewing angle. RGB + active depth sensor. Segmentation had to be manually adjusted for 7 of 128 items, so not fully automatic.", "original_metric": "mean (SD) absolute error and mean (SD) relative absolute error per meal", "original_numbers": "energy: 41.2 kcal (42.5 kcal), relative absolute error 12.7% (10.8%); weight 35.1 g / 14.0%; carbohydrate 14.8%; fat 12.3%; protein 13.0%", "quote_or_location": "Abstract, Results: \"the mean (SD) absolute errors of the app's estimates were ... 41.2 kcal (42.5 kcal; relative absolute error: 12.7% [10.8%]) for energy content\"", "registry_metric": "MAPE", "registry_low_percent": 12.7, "registry_high_percent": 12.7, "range_type": "point", "recomputation_formula": "none — published mean relative absolute error for energy used as is (mean over meals of |pred-ref|/ref = MAPE)", "assumptions": "None. Note SD 10.8% shows wide per-meal spread; no per-meal range published.", "result_status": "measured", "commercial_ties": "none found on check", "ties_note": "\"Conflicts of Interest: None declared.\" Funding: Diabetes Center Bern and Berner Kantonalbank. The developers of SNAQ are thanked in the acknowledgements (\"the developer of the software SNAQ (Aurelian Briner, Nico Previtali, Lukas Frischknecht, and the team) for the pleasant collaboration\") but are not authors; co-author J Dehais is an academic image-analysis researcher, not listed as an SNAQ employee.", "limitations": "Preclinical/laboratory setting, standardised tableware, single depth-capable phone; manual segmentation correction on 5% of items; no free-living meals; energy error therefore an optimistic bound for real use.", "reprint_chain": "none traced", "number_origin": "this study (primary measurement)", "coordinator_note": "", "second_pass_note": "Second pass: the same app (SNAQ) was later tested free-living against doubly labelled water in Serra et al. 2023 — see SCI-19, where the day-level bias was -12.8% with limits of agreement of -1503.8 to +844.5 kcal/day. Do not merge: SCI-01 is a laboratory single-meal test with per-item weighing, SCI-19 is a 7-day free-living test with a biomarker reference and confirmed developer involvement." }, { "record_id": "SCI-02-e1", "status": "registered_not_charted", "reason": "energy error published only in kcal; mean reference energy of the meal sets not published, so no percentage can be derived", "group": "A", "subtype": "peer-reviewed", "independent": "no", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "goFOOD: An Artificial Intelligence System for Dietary Assessment", "product": "goFOOD (two-view mode and stereo/video mode)", "product_version": "2020 research version", "measured_by": "Lu Y, Stathopoulou T, Vasiloglou MF, Pinault LF, Kiley C, Spanakis EK, Mougiakakou S — i.e. the system's own developers", "author_org": "ARTORG Center for Biomedical Engineering Research, University of Bern (system developers); Baltimore VA Medical Center; University of Maryland; Luminis Health", "publisher": "Sensors (Basel) 2020;20(15):4283", "language": "en", "pub_date": "2020-07-31", "test_date": "not stated (MADiMa database collected earlier; Fast Food database collected for this paper)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7436102/", "archive_url": "", "sample_size": "MADiMa: 80 central-European meals / 234 food items; Fast Food: 20 McDonald's meals", "unit": "meal", "reference_and_conditions": "Ground-truth nutrient content of laboratory-prepared meals; comparison arm = experienced dietitians estimating from the same images. Two camera views (or a short video / stereo pair) required; a fiducial reference (credit card) used for scale.", "original_metric": "median absolute error in kcal (IQR)", "original_numbers": "MADiMa: goFOOD 74.9 kcal (40.4–139.3) vs dietitians 180 kcal (119–271). Fast Food: goFOOD two-view 75.9 kcal (27.9–124.7), goFOOD stereo pair 107.8 kcal (54.8–150.9) vs dietitians 55.5 kcal (17–83).", "quote_or_location": "Table 5 / Section 'Results' — median absolute error for calories, goFOOD vs dietitians, MADiMa and Fast Food databases", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "multiple conditions", "recomputation_formula": "not possible — mean/median reference energy of the meal sets is not published in the article, so kcal errors cannot be converted to WAPE", "assumptions": "none", "result_status": "measured", "commercial_ties": "confirmed", "ties_note": "\"Conflicts of Interest: The authors declare no conflict of interest.\" and \"Funding: This research was funded in part by the SV Stiftung.\" — but the authors (Mougiakakou's ARTORG group) are the developers of goFOOD itself; independent: no.", "limitations": "Developer-run evaluation; two-view/stereo capture is not ordinary single-photo use; laboratory meals; median (not mean) error reported, which is favourable to the system.", "reprint_chain": "widely cited; a 27.41% energy error figure circulating in reviews comes from a different goFOOD paper, not from this one", "number_origin": "this study (developers of the tested system)", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-03-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "A", "subtype": "peer-reviewed", "independent": "no", "chart_title": "Nutrition5k", "chart_metric": "Average error per dish", "chart_low_percent": 9.5, "chart_high_percent": 26.1, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "Average calorie error per dish, normalised: photo only 26.1%; with depth 18.8%; with volume 16.5%; with the true dish weight supplied 9.5%.", "chart_limit": "Google researchers evaluating their own models on their own dataset. The 9.5% needs the true weight and is not photo-only.", "title": "Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food", "product": "Google Nutrition5k models (2D direct prediction, depth-as-4th-channel, volume scalar, portion-independent)", "product_version": "research models, 2021", "measured_by": "Thames Q, Karpur A, Norris W, Xia F, Panait L, Weyand T, Sim J (Google Research)", "author_org": "Google Research", "publisher": "CVPR 2021 / arXiv:2103.03375", "language": "en", "pub_date": "2021-03-04 (arXiv); CVPR 2021", "test_date": "not stated; dataset captured 2019-2020", "archive_date": "", "checked_date": "2026-09-05", "url": "https://ar5iv.labs.arxiv.org/html/2103.03375", "archive_url": "", "sample_size": "~5,000 dishes; held-out test split ~10% (about 500 dishes); mean dish energy 255 kcal (SD 220)", "unit": "dish", "reference_and_conditions": "Every ingredient weighed on a scale as the dish was assembled; nutrition computed from USDA entries. Capture rig with RGB video plus Intel RealSense depth. Portion-independent model is evaluated USING GROUND-TRUTH MASS (it predicts kcal per gram, multiplied by true mass), so it is not a photo-only condition.", "original_metric": "Calorie MAE (kcal) and MAE as a percent of the mean (= WAPE)", "original_numbers": "Table 3: baseline (predict dataset mean) 150.8 kcal / 60.2%; 2D direct prediction (RGB) 70.6 kcal / 26.1%; depth as 4th channel (RGB-D) 47.6 kcal / 18.8%; volume scalar 41.3 kcal / 16.5%; 2D portion-independent (true mass supplied) 24.1 kcal / 9.5%", "quote_or_location": "Table 3, caption: \"Mean absolute error (MAE) and mean absolute error as a percent of the respective mean for that field.\"", "registry_metric": "WAPE", "registry_low_percent": 9.5, "registry_high_percent": 26.1, "range_type": "multiple conditions", "recomputation_formula": "none needed — the paper's 'MAE as a percent of the respective mean' is exactly 100 x MAE_kcal / mean_ref_kcal (WAPE)", "assumptions": "Trivial 60.2% baseline (constant mean predictor) excluded from the chart range as it is not a vision method; 9.5% applies only with true dish mass supplied; 26.1% is the photo-only (single RGB) condition; 16.5-18.8% require a depth sensor.", "result_status": "measured", "commercial_ties": "confirmed", "ties_note": "Authors are Google Research employees reporting on their own models and dataset; no COI section in the CVPR paper. Independent: no. Nutrition5k is the validation dataset later cited by the commercial app SnapCalorie (founded by a Nutrition5k author).", "limitations": "Single cafeteria-style capture rig, small mean dish size (255 kcal) so kcal errors look small; test dishes drawn from the same distribution as training; not a consumer app; the best numbers depend on depth hardware or on being told the true mass.", "reprint_chain": "the 70.6 kcal / 26.1% row is reused as the 'Google-nutrition-monocular' baseline in later papers (e.g. Tanabe & Yanai 2025, SCI-09)", "number_origin": "this paper (primary)", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-04-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "A", "subtype": "peer-reviewed", "independent": "no", "chart_title": "CALO mama", "chart_metric": "Minimum error, from average bias", "chart_low_percent": 1.6, "chart_high_percent": 10.1, "chart_is_lower_bound": "yes", "chart_is_claim": "no", "chart_note": "Mean reference 562 kcal. Automatic estimate averaged 505 kcal (−57), after manual correction 571 kcal (+9). That is at least 10.1% and 1.6%; the measured per-meal error is not published.", "chart_limit": "Paid for by the developer, Link & Communication; two authors were its employees, another had a research advisory contract. Peer-reviewed, not independent. Japanese CALO mama is not Calorie Mama.", "title": "Nutrient and Food Group Prediction as Orchestrated by an Automated Image Recognition System in a Smartphone App (CALO mama): Validation Study", "product": "CALO mama (image recognition), two conditions: automatic image recognition only, and image recognition with manual user modification", "product_version": "2021 app version", "measured_by": "Sasaki Y, Sato K, Kobayashi S, Asakura K", "author_org": "Link & Communication Inc (app developer), Kyoto University, Toho University", "publisher": "JMIR Formative Research 2022;6(1):e31875", "language": "en", "pub_date": "2022-01-10", "test_date": "not stated in abstract/results", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8787663/", "archive_url": "", "sample_size": "120 meal samples", "unit": "meal", "reference_and_conditions": "Dietitians observed the cooking process and recorded actual nutrient/food-group content as the gold standard; mean actual meal energy 562 kcal (SD 191).", "original_metric": "mean difference (app minus reference) in kcal", "original_numbers": "image recognition only (Data X): mean 505 kcal (SD 189), difference -57 kcal, P<.001; manually adjusted (Data Y): mean 571 kcal (SD 185), difference +9 kcal, P=.40; reference 562 kcal (SD 191)", "quote_or_location": "Table 2: \"Energy kcal 562 (191) | 505 (189) | -57 | <.001 | 571 (185) | 9 | .40\"", "registry_metric": "bias lower bound", "registry_low_percent": 1.6, "registry_high_percent": 10.1, "range_type": "multiple conditions", "recomputation_formula": "lower_bound_WAPE = 100 x |mean bias| / mean reference = 100 x 57/562 = 10.1% (automatic); 100 x 9/562 = 1.6% (after manual correction)", "assumptions": "Only mean bias is published, so these are LOWER BOUNDS on absolute error; the true absolute error is higher and the upper bound is open.", "result_status": "lower bound", "commercial_ties": "confirmed", "ties_note": "\"This work was conducted using research expenditures from Link & Communication Inc. YS and SK are employees of Link & Communication, Inc.\" — the company publishes CALO mama. Independent: no.", "limitations": "Mean bias only (cancelling of over- and under-estimates); Japanese meals; the 'manual modification' arm is not photo-only estimation.", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "Automatic (10.1%) vs manually corrected (1.6%) are different conditions; both are lower bounds from mean bias.", "second_pass_note": "" }, { "record_id": "SCI-05-e1", "status": "registered_not_charted", "reason": "The 0.1% is a difference of medians between ChatGPT and the reference. A difference of medians is not an error and cannot bound absolute error; the same paper reports 27.8% error on dish mass.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "An Evaluation of ChatGPT for Nutrient Content Estimation from Meal Photographs", "product": "ChatGPT (GPT-4-class, web interface)", "product_version": "not stated beyond 'ChatGPT'", "measured_by": "O'Hara C, Kent G, Flynn AC, Gibney ER, Timon CM", "author_org": "Royal College of Surgeons in Ireland; University College Dublin", "publisher": "Nutrients 2025;17(4):607", "language": "en", "pub_date": "2025-02-07", "test_date": "2024 (ethics approval 2024-07-09)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11858203/", "archive_url": "", "sample_size": "114 meal photographs (38 meals x 3 portion sizes)", "unit": "meal", "reference_and_conditions": "Meals weighed during preparation; composition from McCance & Widdowson via Nutritics. Seven dietitians estimated the same photographs as a human comparator. Photo only, no portion hint.", "original_metric": "percentage difference between median ChatGPT and median actual value per nutrient; Spearman correlation; quartile cross-classification", "original_numbers": "Energy: actual median 524.5 kcal (IQR 361.5-740.5) vs ChatGPT 525 kcal (350-700), difference 0.1%, p=0.219, r=0.115; Spearman 0.73; exact quartile agreement 48.2%, exact+adjacent 88.6%. Meal WEIGHT absolute percentage difference: mean 27.8% (SD 18.5), range 0.2-100%, underestimated in 76.3% of photos. Mean absolute percentage difference across all 16 nutrients: 26.9%.", "quote_or_location": "Table 1 row 'Energy (kcal) 524.5 (361.5-740.5) | 525 (350-700) | 0.1 | 0.219'; Results 3.2: \"a mean +/- SD absolute percentage difference of 27.8% +/- 18.5%\" (meal weight)", "registry_metric": "bias lower bound", "registry_low_percent": 0.1, "registry_high_percent": "", "range_type": "point", "recomputation_formula": "The published 0.1% for energy is a comparison of MEDIANS (group-level bias), not a per-meal absolute error; it is therefore only a lower bound on absolute error, with the upper bound open.", "assumptions": "No per-meal MAPE for energy is published in this paper. The 27.8% is meal WEIGHT, not energy; the 26.9% is an average across 16 nutrients, not energy.", "result_status": "lower bound", "commercial_ties": "none found on check", "ties_note": "\"This research received no external funding.\" \"Conflicts of Interest: The authors declare no conflicts of interest.\" Raw data openly posted on figshare (10.6084/m9.figshare.28271003.v1), so a per-meal WAPE could in principle be recomputed from the deposited data.", "limitations": "Energy agreement at the group level hides large per-meal error: ChatGPT tracked small portions well but significantly underestimated medium and large portions (p<0.001), and mis-estimated meal weight by 27.8% on average.", "reprint_chain": "the '0.1% for energy' figure is repeated by news outlets (news-medical, Feb 2025) as if it were ChatGPT's calorie accuracy — a misreading of a median-vs-median comparison", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-06-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "Fridolfsson · three models", "chart_metric": "Average error per dish", "chart_low_percent": 35.8, "chart_high_percent": 64.2, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "GPT-4o and Claude 3.5 Sonnet: 35.8% average error per dish (MAPE) each; Gemini 1.5 Pro: 64.2%. Underestimation grew with portion size.", "chart_limit": "Published 2025, tested September 2024. Controlled photos with cutlery and weighed portions.", "title": "Performance Evaluation of 3 Large Language Models for Nutritional Content Estimation from Food Images", "product": "ChatGPT-4o; Claude 3.5 Sonnet; Gemini 1.5 Pro", "product_version": "GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro (2024-2025 versions)", "measured_by": "Fridolfsson J, Sjoberg E, Thiwang M, Pettersson S", "author_org": "University of Gothenburg (Departments of Medicine, Food and Nutrition, Sport Science); Sahlgrenska University Hospital", "publisher": "Current Developments in Nutrition 2025, doi 10.1016/j.cdnut.2025.107556", "language": "en", "pub_date": "2025-09-09", "test_date": "not stated; nutrient database reference dated 2022-08-23", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12513282/", "archive_url": "", "sample_size": "52 standardized food photographs (16 individual components, 36 complete meals, 3 portion sizes)", "unit": "dish", "reference_and_conditions": "Direct weighing on a calibrated digital scale; nutrient analysis in Dietist NET (USDA database). Identical prompts to all three models; visible cutlery and plates as the only size reference; no mass given.", "original_metric": "MAPE (%) with 95% CI", "original_numbers": "Energy MAPE: ChatGPT-4o 35.8 (27.3, 45.1); Claude 3.5 Sonnet 35.8 (27.9, 44.7); Gemini 1.5 Pro 64.2 (45.0, 86.9). Weight MAPE 36.3 (ChatGPT) and 37.3 (Claude); Gemini 64.2-109.9 across nutrients.", "quote_or_location": "Table 1: energy MAPE rows \"35.8 (27.3, 45.1)\", \"35.8 (27.9, 44.7)\", \"64.2 (45.0, 86.9)\"", "registry_metric": "MAPE", "registry_low_percent": 35.8, "registry_high_percent": 64.2, "range_type": "multiple results", "recomputation_formula": "none — published MAPE used as is; all three model results kept", "assumptions": "none", "result_status": "measured", "commercial_ties": "none found on check", "ties_note": "\"The authors reported no funding received for this study.\" \"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.\"", "limitations": "Only 52 photographs; Swedish standardized meals; single prompt per model, no prompt engineering; models tested through chat interfaces, not the consumer calorie apps built on them.", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-07-e1", "status": "charted", "reason": "On the chart. Chart values recomputed from the source's own numbers; see chart_note and recomputation_formula.", "group": "A", "subtype": "peer-reviewed", "independent": "unclear", "chart_title": "Rodríguez · ChatGPT-5", "chart_metric": "Average error per dish", "chart_low_percent": 6.9, "chart_high_percent": 35.0, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "Same 195 dishes, different input. Photo only: 20.1–35% across three image sets, 30.5% overall. Photo plus a detailed ingredient list: 6.9–18.6%, 13.9% overall. Average error per dish (MAPE).", "chart_limit": "One study, different inputs. The 6.9% applies to 25 home-cooked dishes with a detailed ingredient list. Text-only runs are left out.", "title": "Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios (Case 1: image only)", "product": "ChatGPT-5 (Plus)", "product_version": "ChatGPT-5 Plus, data-sharing opt-out enabled", "measured_by": "Rodriguez-Jimenez M, Martin-del-Campo-Becerra GD, Sumalla-Cano S, Crespo-Alvarez J, Elio I", "author_org": "Universidad Europea del Atlantico; German Aerospace Center (DLR); Universidad Internacional Iberoamericana", "publisher": "Nutrients 2025;17(22):3613, doi 10.3390/nu17223613", "language": "en", "pub_date": "2025-11-19", "test_date": "2025 (not stated more precisely)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12655113/", "archive_url": "", "sample_size": "195 dishes: Allrecipes.com 96, SNAPMe 74, home-prepared dietitian-weighed 25", "unit": "dish", "reference_and_conditions": "Three reference sources: published recipe nutrition (Allrecipes), the SNAPMe benchmark, and 25 dietitian-weighed home-prepared meals. Case 1 = photograph only, no contextual information, no mass given.", "original_metric": "MAE (kcal) and MAPE (%)", "original_numbers": "Case 1 (image only) all sources combined: MAE 123.03 kcal, MAPE 30.51%. By source: Allrecipes MAE 127.81 / MAPE 35.00; SNAPMe MAE 133.00 / MAPE 28.19; home-prepared MAE 75.14 / MAPE 20.12.", "quote_or_location": "Results tables, Case 1 rows (MAE kcal / MAPE %) for Allrecipes.com, SNAPMe and home-prepared sources", "registry_metric": "MAPE", "registry_low_percent": 20.12, "registry_high_percent": 35.0, "range_type": "multiple results", "recomputation_formula": "none — published per-source MAPE used; combined value 30.51%", "assumptions": "Range spans the three reference sources of the same photo-only condition.", "result_status": "measured", "commercial_ties": "not disclosed", "ties_note": "No explicit funding or COI statement located in the accessible full text of the PMC version; authors are academic (private universities in Spain/Latin America) with no stated link to OpenAI.", "limitations": "Two of three reference sources are not weighed-food ground truth (recipe database and a public photo benchmark); only 25 dishes have dietitian-weighed reference.", "reprint_chain": "cited and beaten by Nakagawa & Yamamoto 2026 (SCI-12) on SNAPMe energy MAE", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-07-e2", "status": "registered_not_charted", "reason": "Merged into the chart row SCI-07-e1: same 195 dishes, photo-only and photo-plus-ingredients conditions shown as one range.", "group": "A", "subtype": "peer-reviewed", "independent": "unclear", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5 (Cases 2-4: added context / text only)", "product": "ChatGPT-5 (Plus)", "product_version": "ChatGPT-5 Plus", "measured_by": "Rodriguez-Jimenez M et al.", "author_org": "Universidad Europea del Atlantico; German Aerospace Center (DLR); Universidad Internacional Iberoamericana", "publisher": "Nutrients 2025;17(22):3613", "language": "en", "pub_date": "2025-11-19", "test_date": "2025", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12655113/", "archive_url": "", "sample_size": "same 195 dishes", "unit": "dish", "reference_and_conditions": "Case 2 = image + non-visual data (fat type/amount, sweetener, dairy fat, meat type); Case 3 = image + full ingredient list with quantities; Case 4 = text only, no image. These are NOT photo-only conditions and must not be merged with Case 1.", "original_metric": "MAE (kcal) and MAPE (%)", "original_numbers": "Combined: Case 2 MAE 92.04 / MAPE 24.35; Case 3 MAE 53.33 / MAPE 13.92; Case 4 (text only) MAE 66.51 / MAPE 18.05. Per source, Case 2: 28.85 / 22.17 / 13.55; Case 3: 18.64 / 10.16 / 6.92; Case 4: 25.76 / 10.13 / 11.91.", "quote_or_location": "Results tables, Case 2/3/4 rows", "registry_metric": "MAPE", "registry_low_percent": 6.92, "registry_high_percent": 28.85, "range_type": "multiple conditions", "recomputation_formula": "none — published MAPE", "assumptions": "Range covers all context-assisted cases and all three sources; the low end (6.92%) requires the model to be told the full ingredient list with quantities.", "result_status": "measured", "commercial_ties": "not disclosed", "ties_note": "see SCI-07-e1", "limitations": "Shows that most of the accuracy comes from information the photo does not contain; the best figures should never be quoted as photo accuracy.", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-08-e1", "status": "charted", "reason": "On the chart. Chart values recomputed from the source's own numbers; see chart_note and recomputation_formula.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "Tanabe · six models", "chart_metric": "Average error per dish", "chart_low_percent": 39.8, "chart_high_percent": 129.5, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "Six models tested on the Nutrition5k photos, with and without fine-tuning: 129.5, 92.8, 55.7, 46.7, 41.5 and 39.8% average error per dish (MAPE).", "chart_limit": "Figures quoted from earlier papers are not counted as new tests. Volume-injection experiments are a different protocol and are left out.", "title": "Reasoning-Driven Food Energy Estimation via Multimodal Large Language Models", "product": "GPT-4V, GPT-4o, LLaVA-1.5-7B/13B (baseline and fine-tuned), FoodLMM", "product_version": "GPT-4V / GPT-4o as of late 2024", "measured_by": "Tanabe H, Yanai K", "author_org": "The University of Electro-Communications, Tokyo", "publisher": "Nutrients 2025;17(7):1128", "language": "en", "pub_date": "2025-03-24", "test_date": "not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11990770/", "archive_url": "", "sample_size": "Nutrition5k: 3,265 annotated images (train 2,759 / test 506)", "unit": "dish", "reference_and_conditions": "Nutrition5k ground truth (every ingredient weighed). RGB images; a 'with volume' variant supplies an estimated volume cue. Mass not given to the model.", "original_metric": "MAE (kcal) and MAPE (%)", "original_numbers": "Google-nutrition-monocular 70.6 / 26.1 (cited prior work); LLaVA-1.5-7B 178.8 / 129.5; LLaVA-1.5-13B 177.1 / 92.8; GPT-4V 80.7 / 55.7; GPT-4o 82.7 / 46.7; GPT-4V with volume 83.8 / 54.1; GPT-4o with volume 78.8 / 43.4; FoodLMM FT 67.3 / 26.6; LLaVA-1.5-7B FT 74.2 / 41.5; LLaVA-1.5-13B FT 64.3 / 39.8", "quote_or_location": "Results table 'MAE / MAPE' per model (Google-nutrition-monocular, LLaVA-1.5, GPT-4V, GPT-4o, FoodLMM FT)", "registry_metric": "MAPE", "registry_low_percent": 26.6, "registry_high_percent": 129.5, "range_type": "multiple results", "recomputation_formula": "none — published MAPE for every model evaluated in this paper", "assumptions": "The 26.1% Google row is prior work cited for comparison and is not counted in this paper's range; fine-tuned models (FT) are trained on Nutrition5k itself, so their figures are in-domain.", "result_status": "measured", "commercial_ties": "none found on check", "ties_note": "\"This work was supported by JSPS KAKENHI Grant Numbers, 22H00540 and 22H00548.\" \"The authors declare no conflicts of interest.\"", "limitations": "Out-of-the-box general LLMs on Nutrition5k give MAPE 43-56% (GPT-4V/4o); the low figures belong to models fine-tuned on the same dataset; single-plate cafeteria dishes, mean energy only ~255 kcal.", "reprint_chain": "Google-nutrition-monocular row -> Nutrition5k CVPR 2021 (SCI-03)", "number_origin": "this study (GPT/LLaVA rows measured here; Google row reprinted from Thames et al. 2021)", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-09-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "A", "subtype": "preprint", "independent": "yes", "chart_title": "Coburn · eight models", "chart_metric": "Average error per dish", "chart_low_percent": 23.3, "chart_high_percent": 86.3, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "Eight models on 806 photos that contain a scale marker: 23.3–86.3% average error per dish (MAPE) in the baseline protocol.", "chart_limit": "Preprint. The scale marker in every photo helps with portion size. Prompts enriched with metadata are a separate condition. Model averages, not per-meal limits.", "title": "Evaluating Large Multimodal Models for Nutrition Analysis: A Benchmark Enriched with Contextual Metadata", "product": "GPT-4o, GPT-4.1, Claude 3.7 Sonnet, Gemini 2.5 Pro, Gemma-3-27B, Janus-Pro-7B, Llama-3.2-Vision-90B, Qwen2.5-VL-72B", "product_version": "model versions as of mid-2025", "measured_by": "Coburn B, He J, Rollo ME, Dhaliwal SS, Kerr DA, Zhu F", "author_org": "Purdue University; MIT; Curtin University", "publisher": "arXiv:2507.07048v1", "language": "en", "pub_date": "2025-07-09", "test_date": "2025 (ACETADA images from an earlier controlled-feeding study)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://arxiv.org/html/2507.07048v1", "archive_url": "", "sample_size": "806 before-meal images from 152 adults (ACETADA, Perth, Western Australia)", "unit": "meal", "reference_and_conditions": "Dietitian-verified weights to 0.1 g and nutrient values from a controlled-feeding study; RGB images with fiducial markers for scale; no depth; portion mass not supplied to the models.", "original_metric": "calorie MAE (kcal) and MAPE (%) for a baseline prompt, and changes with contextual metadata (GPS, timestamp, food name) and reasoning prompts", "original_numbers": "Baseline calorie MAE / MAPE: Claude-3.7-Sonnet 181.68 / 23.88; GPT-4.1 170.02 / 23.29; GPT-4o 165.77 / 24.48; Gemini-2.5-Pro 211.04 / 34.11; Gemma3 187.54 / 27.67; Janus-Pro 477.79 / 86.29; Llama-3.2-Vision 496.47 / 66.22; Qwen2.5-VL 276.04 / 33.90. Best metadata combinations cut MAE by ~76 kcal on average (MAPE reductions 1.5-52.2 points).", "quote_or_location": "Table II caption: \"Impact of Metadata Integration on LMM Nutritional Estimation Error...\"; rows e.g. \"Claude-3.7-Sonnet | Closed | Baseline | 181.68 | 23.88\", \"Janus-Pro | Open | Baseline | 477.79 | 86.29\"", "registry_metric": "MAPE", "registry_low_percent": 23.29, "registry_high_percent": 86.29, "range_type": "multiple results", "recomputation_formula": "none — published baseline MAPE for all eight models kept", "assumptions": "Baseline (photo + simple prompt) condition used for the chart; metadata-enriched results are a different condition (extra non-photo information).", "result_status": "measured", "commercial_ties": "none found on check", "ties_note": "Academic authors (Purdue, MIT, Curtin); no COI statement in the preprint; models evaluated are third-party.", "limitations": "Preprint, not peer reviewed at time of check; images include fiducial markers (a scale cue ordinary users do not have); mean reference energy not published, so WAPE cannot be recomputed from the MAE.", "reprint_chain": "none traced", "number_origin": "this preprint", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-10-e1", "status": "registered_not_charted", "reason": "Withheld: published Table 2 is internally inconsistent (ChatGPT energy MAE 38.5 kcal exceeds the RMSE of about 26 kcal implied by the same table's SDs, correlation and bias; carbohydrate MAE 6.8 g is below its absolute mean bias 11.9 g). Held back pending clarification; no allegation of fabrication.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Accuracy of AI-Based Nutrient Estimation from Standardized Hospital Meal Images: A Comparison with Registered Dietitians", "product": "10 AI models incl. ChatGPT-4o, Gemini 1.5 Pro, Foodita; comparator = 10 registered dietitians", "product_version": "model versions as of 2025", "measured_by": "Isobe T, Zhang LW, Murakami H, Kadono M, Aso M, Kayashita A, Kayashita J", "author_org": "Prefectural University of Hiroshima, Graduate School of Comprehensive Scientific Research", "publisher": "Nutrients 2026;18(6):966, doi 10.3390/nu18060966", "language": "en", "pub_date": "2026-03-01", "test_date": "not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13029357/", "archive_url": "", "sample_size": "15 standardized hospital meals (breakfast/lunch/dinner over 5 non-consecutive days); mean reference energy 605.2 kcal (SD 26.5)", "unit": "meal", "reference_and_conditions": "Ground truth by direct weighing of all ingredients after cooking (Standard Tables of Food Composition in Japan, 8th rev.). Photographs taken at a 90-degree vertical angle under 500 lux with a smartphone. Single RGB photo, no mass given.", "original_metric": "Mean Bias (kcal and %), MAE (kcal), Pearson r, share within +/-5% and +/-10%", "original_numbers": "Energy (ground truth 605.2 +/- 26.5 kcal): RD mean bias -15.4 kcal (-2.5%), MAE 22.4, r 0.92, within +/-10% 86.7%; ChatGPT-4o bias -12.2 (-2.0%), MAE 38.5, r 0.89, within +/-10% 73.3%; Gemini 1.5 Pro bias -17.6 (-2.9%), MAE 42.1, r 0.85, within +/-10% 60.0%; Foodita bias -6.0 (-1.0%), MAE 48.9, r 0.82, within +/-10% 46.7%. The other 7 of 10 models failed the screen (energy MAE >= 50 kcal or r <= 0.80); energy r across all 10 models ranged 0.21-0.89.", "quote_or_location": "Table 2: \"Energy (Kcal) 605.2 +/- 26.5 | RD (Mean) 589.8 +/- 35.2 -15.4 -2.5 22.4 0.92 ... | ChatGPT 593.0 +/- 44.3 -12.2 -2.0 38.5 0.89 ... 73.3% | Gemini 587.6 +/- 55.5 -17.6 -2.9 42.1 ... 60.0% | Foodita 599.2 +/- 92.8 -6.0 -1.0 48.9 ... 46.7%\"", "registry_metric": "WAPE", "registry_low_percent": 6.4, "registry_high_percent": 8.1, "range_type": "multiple results", "recomputation_formula": "WAPE = 100 x MAE_kcal / mean_ref_kcal = 100 x 38.5/605.2 = 6.4% (ChatGPT-4o); 42.1/605.2 = 7.0% (Gemini 1.5 Pro); 48.9/605.2 = 8.1% (Foodita); dietitians 22.4/605.2 = 3.7%", "assumptions": "Only the three screened 'top performers' have published energy MAE; the remaining seven models had energy MAE >= 50 kcal (>= 8.3% WAPE) and are reported only in supplementary material, so the upper end of the range is OPEN.", "result_status": "exact recomputation", "commercial_ties": "none found on check", "ties_note": "\"Conflicts of Interest The authors declare no conflicts of interest.\" \"Funding Statement This research received no external funding.\"", "limitations": "Only 15 meals, all from a single standardized 1800 kcal/day Japanese hospital menu with narrow energy spread (SD 26.5 kcal) and controlled lighting/angle — an easy, low-variance test; lipid estimates were systematically overestimated by >20% by all AI models.", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-11-e1", "status": "registered_not_charted", "reason": "no energy MAPE/WAPE obtainable from the accessible text", "group": "A", "subtype": "peer-reviewed", "independent": "unclear", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Prompt Engineering and Model Selection for LLM-Based Nutritional Estimation from Food Images: A Multi-Dataset Investigation", "product": "Claude Haiku 4.5, Claude Sonnet 4.6, Claude Opus 4.6", "product_version": "Haiku 4.5 / Sonnet 4.6 / Opus 4.6", "measured_by": "Nakagawa S, Yamamoto A", "author_org": "Research Institute of Info-Communication Medicine (RinCOM), Tokyo", "publisher": "Nutrients 2026;18(12):2017, doi 10.3390/nu18122017", "language": "en", "pub_date": "2026-06-21", "test_date": "not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://doi.org/10.3390/nu18122017", "archive_url": "", "sample_size": "NutriImage 691 Japanese meal photos (dietitian-validated); SNAPMe 1463 US meal photos; Japan Branded Food Database 989-1000 packaged-product images", "unit": "dish", "reference_and_conditions": "Dietitian-validated ground truth (NutriImage), public SNAPMe benchmark, packaged-product label values (JBFD). Two prompts compared: default vs a visual-estimation prompt forbidding the model to read text/numbers in the image.", "original_metric": "R2, MedAPE (%), MAE (kcal)", "original_numbers": "SNAPMe energy MAE: Sonnet 116.9 kcal vs 123.0 kcal for the ChatGPT-5 study (-4.9%); energy R2 on JBFD 0.23 (Haiku) to 0.60 (Sonnet); Sonnet vs Opus MedAPE difference 1-3 percentage points; salt equivalent MedAPE 34-64%. Models over-estimate caloric nutrients on NutriImage and under-estimate on SNAPMe (under-estimation concentrated in larger meals).", "quote_or_location": "Abstract (Europe PMC, PMID 42356404): \"On SNAPMe, Sonnet achieved lower energy MAE (116.9 vs. 123.0 kcal, -4.9%)... Salt equivalent showed consistently poor accuracy (MedAPE 34-64%).\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "multiple conditions", "recomputation_formula": "not possible from the accessible text — per-dataset energy MedAPE values are in the full text, which MDPI serves only behind a bot block; no PMC deposit exists (PMID 42356404, no PMCID)", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "not disclosed", "ties_note": "Abstract-level record only; funding/COI statements not retrievable (MDPI returned HTTP 403 to both WebFetch and curl; Europe PMC holds abstract only). Authors are a private Tokyo research institute; no stated link to Anthropic.", "limitations": "Full text not verifiable through open channels at check time; the only directly comparable energy figure is the SNAPMe MAE of 116.9 kcal; no mean reference energy published in the abstract, so no WAPE.", "reprint_chain": "explicitly benchmarks itself against the ChatGPT-5 study (SCI-07) on SNAPMe", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-12-e1", "status": "registered_not_charted", "reason": "equivalence-bound/CV reporting; no per-meal absolute energy error published", "group": "A", "subtype": "peer-reviewed", "independent": "unclear", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Customized multimodal Diabot-GPT-4o enhances accuracy of image-based dietary assessments in dietetic trainees in Taiwan: validation against weighed food records", "product": "Diabot (customized GPT-4o) and stock GPT-4o, each with and without food-name input (DB, DBFN, 4o, 4oFN)", "product_version": "GPT-4o, 2025", "measured_by": "Chen YJ, Chang CC, Hoang YN, Lin AW, Lin WL, Lin CY, Patricia E, Tissadharma JC, Kuanca J, Nobelta N, Theo KA, Ho DKN, Wei PH, Chang JS", "author_org": "Taipei Medical University, School of Nutrition and Health Sciences", "publisher": "The American Journal of Clinical Nutrition, doi 10.1016/j.ajcnut.2025.10.013", "language": "en", "pub_date": "2025-10-24", "test_date": "not stated; 3-day weighed food records over 171 days", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pubmed.ncbi.nlm.nih.gov/41138916/", "archive_url": "", "sample_size": "714 food images from 3-day weighed food records across 171 days, 57 young adults", "unit": "meal", "reference_and_conditions": "Weighed food records (WFRs) by dietetic trainees in free-living conditions; Bland-Altman with equivalence testing at +/-10%, +/-15%, +/-20% bounds. Free-living photos, no standardized capture.", "original_metric": "equivalence bounds (%) and coefficient of variation, not MAE/MAPE", "original_numbers": "\"All AI configurations provided accurate estimates of portion sizes (+/-10%-15%, coefficient of variation [CV]: 13%), energy (+/-10%-20%, CV: 14%), and carbohydrates (CHOs; +/-15%-20%, CV: 15%) but showed less consistency for fats (+/-10%-22%, CV: 24%) and proteins (+/-10%->20.2%, CV: 18%)\". Image-only food recognition: Diabot 74% vs GPT-4o 59% of items.", "quote_or_location": "Abstract, Results (Europe PMC, PMID 41138916)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "multiple conditions", "recomputation_formula": "not applicable — equivalence bounds on mean difference are not an absolute-error metric and cannot be converted to WAPE/MAPE", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "not disclosed", "ties_note": "Diabot is a chatbot customized by the authors' own group, so this arm is developer-evaluated; full COI text not retrievable (Elsevier returned HTTP 403). Independent for the stock GPT-4o arm, not for the Diabot arm.", "limitations": "Reports equivalence within bounds, not error magnitude; a mean difference inside +/-10% is compatible with large per-meal errors; participants were 95% dietetics students photographing their own weighed food (best case for image quality and prompts).", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-13-e1", "status": "charted", "reason": "On the chart. Chart values recomputed from the source's own numbers; see chart_note and recomputation_formula.", "group": "A", "subtype": "peer-reviewed", "independent": "no", "chart_title": "Passio-based research app", "chart_metric": "Minimum error, from average daily underestimation", "chart_low_percent": 20.1, "chart_high_percent": 20.1, "chart_is_lower_bound": "yes", "chart_is_claim": "no", "chart_note": "Average underestimation 1814 kJ/day against a measured expenditure of 9017 kJ/day: at least 20.1% missing. The paper's 15% is the mean of individual ratios.", "chart_limit": "The academic authors built the app on Passio's engine; public and charitable grants paid for it, Passio supplied the algorithm in kind. Authors' own validation of their workflow.", "title": "An automated image-based dietary assessment application: a pilot study", "product": "iOS app built on Passio Nutrition AI computer-vision software", "product_version": "2024-2025 research build", "measured_by": "Lee L, Bishop R, Stanley J (He Rourou Whai Painga Consortium)", "author_org": "University of Otago Wellington; Centre for Endocrine, Diabetes and Obesity Research", "publisher": "Journal of Nutritional Science 2025, doi 10.1017/jns.2025.10045", "language": "en", "pub_date": "2025-11-04", "test_date": "not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12658290/", "archive_url": "", "sample_size": "29 participants recruited, 19 completed all components", "unit": "day", "reference_and_conditions": "Free-living use; comparison against Intake24 24-h recall and against estimated energy expenditure from indirect calorimetry plus wrist accelerometry (not weighed food).", "original_metric": "mean bias in kJ/day with 95% CI", "original_numbers": "App vs estimated energy expenditure: mean bias -1814 kJ/day (95% CI -3012 to -615, p=0.005), about a 15% underestimate; 24-h recall vs EE: -1715 kJ/day (95% CI -3237 to -193, p=0.029), also about 15%; app vs 24-h recall: +783 kJ/day (95% CI -875 to 2441, p=0.33)", "quote_or_location": "Results / abstract: app vs estimated EE mean bias -1814 kJ (95% CI -3012 to -615, p = 0.005)", "registry_metric": "bias lower bound", "registry_low_percent": 15.0, "registry_high_percent": "", "range_type": "point", "recomputation_formula": "paper's own ~15% underestimate relative to estimated energy expenditure; a mean bias, hence a LOWER BOUND on absolute error", "assumptions": "Unit is a day of free-living intake, not a single dish; the deficit includes missed/unlogged eating occasions, not only photo estimation error.", "result_status": "lower bound", "commercial_ties": "confirmed", "ties_note": "\"The authors have no conflicts of interest to declare\"; funded by the Health Research Council of New Zealand and the Maurice and Phyllis Paykel Trust; Passio Inc provided the computer-vision algorithm as in-kind support.", "limitations": "Pilot, n=19 completers; reference is estimated expenditure (itself imprecise), not weighed food; day-level under-reporting conflates photo error with logging omissions — the app and the 24-h recall under-reported by the same ~15%.", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-14-e1", "status": "charted", "reason": "On the chart. Chart values recomputed from the source's own numbers; see chart_note and recomputation_formula.", "group": "A", "subtype": "conference abstract + press release", "independent": "yes", "chart_title": "NIH · four apps", "chart_metric": "Minimum error, from average underestimation", "chart_low_percent": 27.5, "chart_high_percent": 37.6, "chart_is_lower_bound": "yes", "chart_is_claim": "no", "chart_note": "Mean reference 918 kcal per meal. Average underestimation: MyFitnessPal −327, Lose It! −333, Cal AI −345, Appediet −252 kcal. Divided by 918 that is at least 35.6%, 36.3%, 37.6% and 27.5% of a meal; the true per-meal error is larger, because errors in both directions partly cancel in an average.", "chart_limit": "Conference abstract, not yet a full paper. NIH funding. The 102-meal analysis is separate from the further meals mentioned in press coverage.", "title": "Photo-based calorie-tracking apps may underestimate energy in meals (NUTRITION 2026 conference abstract, NIDDK)", "product": "MyFitnessPal, Lose It!, Cal AI, Appediet (photo features)", "product_version": "app versions as used in 2026, not specified", "measured_by": "Aaron Hengist (postdoctoral visiting fellow, NIDDK) and Olivia Charles (postbaccalaureate fellow, NIDDK)", "author_org": "National Institute of Diabetes and Digestive and Kidney Diseases, NIH", "publisher": "American Society for Nutrition, NUTRITION 2026 (National Harbor, MD, 25-28 July 2026); press release via EurekAlert!", "language": "en", "pub_date": "2026-07-26", "test_date": "2026 (meals from an ongoing NIDDK controlled feeding trial)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.eurekalert.org/news-releases/1136415", "archive_url": "", "sample_size": "102 meals in the primary analysis; \"more than 200 additional meals\" in a follow-up analysis", "unit": "meal", "reference_and_conditions": "Meals prepared in a controlled metabolic kitchen with ingredients \"carefully weighed to the nearest 0.1 gram\"; standardized photographs submitted to each app.", "original_metric": "mean underestimation in kcal per meal per app; overall percentage underestimate", "original_numbers": "\"All four apps underestimated the calorie content of meals by about 250 to 345 calories, on average, and fat by about 30 grams.\" Per app (via Healio, quoting Hengist): Appediet -252 kcal, MyFitnessPal -327 kcal, Lose It! -333 kcal, Cal AI -345 kcal; \"the AI photograph-based features in these apps underestimated meals' energy content by about 33%\".", "quote_or_location": "EurekAlert!: \"All four apps underestimated the calorie content of meals by about 250 to 345 calories, on average, and fat by about 30 grams.\"; Healio: \"the AI photograph-based features in these apps underestimated meals' energy content by about 33%\"", "registry_metric": "bias lower bound", "registry_low_percent": 25.4, "registry_high_percent": 34.8, "range_type": "multiple results", "recomputation_formula": "lower_bound_WAPE = 100 x |mean bias| / mean reference. Mean reference energy is not published; it is inferred as ~991 kcal from the pair (MyFitnessPal -327 kcal <-> ~33%). Per app: 252/991=25.4%, 327/991=33.0%, 333/991=33.6%, 345/991=34.8%.", "assumptions": "ESTIMATED recomputation: assumes the reported ~33% corresponds to the ~327 kcal app-level bias and that all four apps saw the same meal set. These are mean biases, so they are LOWER BOUNDS on absolute error; upper bound open.", "result_status": "estimated recomputation", "commercial_ties": "none found on check", "ties_note": "NIH/NIDDK intramural work; no commercial funding stated; the tested apps are third-party. No COI statement is published with a conference abstract.", "limitations": "Conference abstract, not peer reviewed (\"abstracts... have not generally undergone the same peer review process required for publication\"); no full method, no per-meal spread, no mean reference energy; per-app kcal figures come from a journalist's quotation of the researcher, not from a published table.", "reprint_chain": "EurekAlert!/ASN press release -> news-medical.net, ScienceDaily, MedicalXpress, Medscape, Healio, Nutritional Outlook, and German/French consumer press (pharmazeutische-zeitung.de, aponet.de). Ten repeats of one measurement, not ten checks.", "number_origin": "Hengist & Charles, NIDDK (measurement); numbers first published in the ASN press release of 2026-07-26", "coordinator_note": "Abstract at NUTRITION 2026 (not a full paper). Per-app kcal figures exist only in a Healio quotation of the author; mean reference (~991 kcal) is inferred from the pair 327 kcal / ~33%. Treat the percentages as approximate lower bounds.", "second_pass_note": "" }, { "record_id": "SCI-15-e1", "status": "registered_not_charted", "reason": "Review, not an experiment: spread across 52 heterogeneous studies with different error formulas and ground truths; keep as context, not as a chart point.", "group": "A", "subtype": "review / meta-analysis", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review", "product": "various AI image-based dietary assessment systems (aggregate)", "product_version": "n/a", "measured_by": "Shonkoff E, Cara KC, Pei X, Chung M, Kamath S, Panetta K, Hennessy E (review of 52 primary studies)", "author_org": "Tufts University (Friedman School / School of Medicine); Tufts School of Engineering", "publisher": "Annals of Medicine 2023;55(2):2273497", "language": "en", "pub_date": "2023-12-07", "test_date": "primary studies published 2010-2023", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10836267/", "archive_url": "", "sample_size": "52 peer-reviewed articles", "unit": "dish", "reference_and_conditions": "Aggregation across studies; ground truth in the primary studies was nutrient-table calculation (51%) or weighed food (27%). Error definitions vary between the pooled studies.", "original_metric": "relative error (%) for calories/energy, ranges across studies", "original_numbers": "Calories, relative error: average range across studies 0.10% to 38.3%; full range across all papers 0.00% to 79.6%. Volume relative error: averages 0.09-33%, full range 0.00-34.20%. Most frequently reported outcomes: relative error for volume (40%) and calories/energy (31%).", "quote_or_location": "Results, error-summary section: calories relative error average range 0.10-38.3%, overall range 0.00-79.6%", "registry_metric": "MAPE-like", "registry_low_percent": 0.1, "registry_high_percent": 79.6, "range_type": "reported spread", "recomputation_formula": "none — the review's own reported spread across primary studies", "assumptions": "This is a spread over heterogeneous studies with different error formulas, ground truths and difficulty (single food vs mixed meals); the low end mostly comes from single-food images. Not a single experiment.", "result_status": "measured", "commercial_ties": "none found on check", "ties_note": "\"No potential conflict of interest was reported by the author(s).\" Supported by NIH grant R21CA250024. The review notes the underlying literature itself is 33% privately funded.", "limitations": "Reviews rather than measures; error definitions are not harmonised across the pooled studies, so the range must not be read as one comparable metric; averages tended to be smaller for single-food images than for multiple-food images.", "reprint_chain": "secondary source by construction; primary numbers belong to the 52 included papers", "number_origin": "aggregation of 52 primary studies", "coordinator_note": "Review, not an experiment: spread across 52 heterogeneous studies with different error formulas and ground truths; keep as context, not as a chart point.", "second_pass_note": "" }, { "record_id": "SCI-16-e1", "status": "registered_not_charted", "reason": "Meta-analysis of image-ASSISTED and image-based methods (largely human-coded photos), day unit, 2000 kcal scenario denominator; not an automatic photo-calorie experiment.", "group": "A", "subtype": "review / meta-analysis", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Validity of image-based dietary assessment methods: A systematic review and meta-analysis", "product": "image-assisted / image-based dietary assessment methods (aggregate, mostly with human review)", "product_version": "n/a", "measured_by": "Ho DKN, Tseng SH, Wu MC, Shih CK, Atika AP, Chen YC, Chang JS", "author_org": "Taipei Medical University", "publisher": "Clinical Nutrition 2020;39(10):2945-2959, doi 10.1016/j.clnu.2020.08.002", "language": "en", "pub_date": "2020-08-12", "test_date": "primary studies up to 2020", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pubmed.ncbi.nlm.nih.gov/32839035/", "archive_url": "", "sample_size": "13 studies, 606 participants", "unit": "day", "reference_and_conditions": "Reference methods: doubly labelled water (DLW), 24-h dietary recall, and estimated/weighed food records.", "original_metric": "weighted mean difference (WMD) in energy intake, kcal/day", "original_numbers": "Overall WMD -179.32 kcal/day (95% CI -269.50 to -89.15; I2=89%); vs DLW WMD -448.04 kcal/day (95% CI -755.52 to -140.56; I2=95%)", "quote_or_location": "Abstract, Results: \"The overall weighted mean difference (WMD) in EI showed significant under-reporting (WMD = -179.32 kcal, 95% confidence interval (CI): -269.50 to -89.15 kcal; I2 = 89%), with the greatest difference observed between tests and DLW (WMD = -448.04 kcal, 95% CI: -755.52 to -140.56 kcal; I2 = 95%)\"", "registry_metric": "bias lower bound", "registry_low_percent": 9.0, "registry_high_percent": 22.4, "range_type": "multiple conditions", "recomputation_formula": "scenario recomputation: 100 x 179.32/2000 = 9.0% (all references) and 100 x 448.04/2000 = 22.4% (vs DLW), per day", "assumptions": "SCENARIO RECOMPUTATION using a 2000 kcal/day denominator because no common daily reference mean is published; unit is a DAY, not a dish. Mean differences are lower bounds on absolute error; under-reporting includes unlogged eating occasions, not only photo estimation error.", "result_status": "lower bound", "commercial_ties": "none found on check", "ties_note": "COI/funding text not retrieved (Elsevier HTTP 403); abstract via Europe PMC. Academic authors; no commercial product under test.", "limitations": "Most included methods are image-ASSISTED (a human reviews the photo), not automatic photo-to-calorie estimation, so this is a bound on the human-in-the-loop paradigm; very high heterogeneity (I2 89-95%).", "reprint_chain": "secondary source", "number_origin": "meta-analysis of 13 primary studies", "coordinator_note": "Meta-analysis of image-ASSISTED and image-based methods (largely human-coded photos), day unit, 2000 kcal scenario denominator; not an automatic photo-calorie experiment.", "second_pass_note": "" }, { "record_id": "SCI-17-e1", "status": "registered_not_charted", "reason": "food recognition accuracy, not calorie error — the paper states none of the platforms could estimate the amount of food", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study", "product": "Calorie Mama API, Bitesnap, Foodvisor, Clarifai, IBM Watson Recognition, LogMeal, Google Vision API", "product_version": "2019-2020 API versions", "measured_by": "Van Asbroeck S, Matthys C", "author_org": "KU Leuven, Department of Chronic Diseases and Metabolism", "publisher": "JMIR Formative Research 2020;4(12):e15602", "language": "en", "pub_date": "2020-12-07", "test_date": "2019-2020", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7752530/", "archive_url": "", "sample_size": "185 images (30 simple plain foods, 36 simple processed, 47 beverages, 72 mixed dishes), each in 6 capture settings", "unit": "dish", "reference_and_conditions": "Known food identity as reference; the study explicitly reports that no platform could estimate the AMOUNT of food, so no energy error could be computed.", "original_metric": "top-1 and top-5 food recognition accuracy (%)", "original_numbers": "Top-1 accuracy 63% (Calorie Mama API) down to 9% (Google Vision API); top-5 accuracy 24% to 88%; Bitesnap 48.9% top-1 / 71.0% top-5; Foodvisor 46.2% top-1 / 71.5% top-5", "quote_or_location": "Results: \"None of the platforms were capable of estimating the amount of food.\"; top-1 accuracy \"63% for the application programming interface (API) of the Calorie Mama app to 9% for the Google Vision API\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "multiple results", "recomputation_formula": "n/a", "assumptions": "none", "result_status": "measured", "commercial_ties": "none found on check", "ties_note": "\"Conflicts of Interest: None declared.\" Funded by EIT Food (SmartFoodLogging, KAVA 18102-19091).", "limitations": "Food IDENTIFICATION only. Kept in the registry because these recognition figures are routinely quoted as if they were calorie accuracy.", "reprint_chain": "the 62.9%/87.6% Calorie Mama figures are widely reprinted in reviews as 'app accuracy'", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-18-e1", "status": "charted", "reason": "On the chart. Chart values recomputed from the source's own numbers; see chart_note and recomputation_formula.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "Li · four apps", "chart_metric": "Error of the six-dish total", "chart_low_percent": 15.4, "chart_high_percent": 59.3, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "Our calculation from the whole-dish rows of Table 10, six dishes all four apps handled: MyFitnessPal 59.3%, HealthifyMe 39.6%, Foodvisor 26.6%, Fastic 15.4% of the total reference calories (WAPE).", "chart_limit": "Small shared subset; milk tea left out because Foodvisor gave no result. Dietitian food records are the reference. No funding or conflicts declared.", "title": "Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care", "product": "MyFitnessPal 24.10.0, Fastic 1.165.0, Foodvisor 5.15.0-1, HealthifyMe 11.1.0 (the four of seven AI image-recognition apps that produce an automatic energy value)", "product_version": "app versions as listed, tested 2024", "measured_by": "Li X, Yin A, Choi HY, Chan V, Allman-Farinelli M, Chen J", "author_org": "University of Sydney, Discipline of Nutrition and Dietetics; Charles Perkins Centre", "publisher": "Nutrients 2024;16(15):2573, doi 10.3390/nu16152573", "language": "en", "pub_date": "2024-08-05", "test_date": "2024 (apps screened and tested between 2024-04 and 2024-07)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11314244/", "archive_url": "", "sample_size": "22 images (16 food, 6 drink) containing 39 food components and 7 mixed dishes, uploaded to each app", "unit": "dish", "reference_and_conditions": "Three-day food records for Western, Asian and Recommended dietary patterns constructed by dietitians and analysed in Foodworks.online; images of those foods photographed and submitted to each app's AI feature. Photo only, fully automatic energy output, no manual correction.", "original_metric": "mean energy difference (%) per app versus the dietitian food record; also food-component identification accuracy (%)", "original_numbers": "Mean energy difference: MyFitnessPal -3%, HealthifyMe +8%, Fastic +44%, Foodvisor -47%. Per-dish examples: 'Eggs on toast with butter' underestimated 35% (MyFitnessPal, butter omitted) and 73% (Foodvisor); 'Boiled Egg' overestimated 5% (Foodvisor) to 37% (HealthifyMe); 'Potato Chips' -12% to -16%. Separate metric: food-component identification accuracy MyFitnessPal 97%, Fastic 92%, HealthifyMe 90%, Foodvisor 87%, Lose It! and FatSecret 46%.", "quote_or_location": "Section 3.4.2: \"MyFitnessPal ... and Foodvisor ... underestimated energy output (mean energy difference, -3% and -47%, respectively). HealthifyMe ... and Fastic ... had ... mean energy difference of 8% and 44%, respectively.\"", "registry_metric": "bias lower bound", "registry_low_percent": 3.0, "registry_high_percent": 47.0, "range_type": "multiple results", "recomputation_formula": "absolute value of each app's published mean energy difference; a signed mean, hence a LOWER BOUND on each app's absolute error", "assumptions": "Mean signed differences allow over- and under-estimates to cancel, so true absolute error per dish is higher; the per-dish figures quoted (up to 73%) show the spread. Identification accuracy (97%, 92%...) is a DIFFERENT metric and must not be read as calorie accuracy — the app with 97% identification still mis-stated energy on mixed dishes.", "result_status": "lower bound", "commercial_ties": "none found on check", "ties_note": "\"This research received no external funding.\" \"The authors declare no conflicts of interest.\" Academic dietetics group; apps are third-party commercial products.", "limitations": "Only 22 images; no weighed meals (reference is a dietitian-constructed food record analysed in Foodworks); free-tier app features only; single submission per image so no test-retest.", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-19-e1", "status": "charted", "reason": "On the chart. Chart values recomputed from the source's own numbers; see chart_note and recomputation_formula.", "group": "A", "subtype": "peer-reviewed", "independent": "no", "chart_title": "Serra · SNAQ", "chart_metric": "Minimum error, from average daily underestimation", "chart_low_percent": 14.8, "chart_high_percent": 14.8, "chart_is_lower_bound": "yes", "chart_is_claim": "no", "chart_note": "App diary averaged 1905.5 kcal/day against a measured expenditure of 2235.2 kcal/day: at least 14.8% missing. The paper's own −12.8% is the mean of individual percentages.", "chart_limit": "Academic funding; SNAQ was paid to build the research version. Expenditure as the intake reference assumes stable weight; skipped entries count against the app.", "title": "Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water", "product": "SNAQ study (customized research build of the commercial SNAQ app), iPhone X/11/12 with depth-sensing camera", "product_version": "SNAQ study, custom build for this trial", "measured_by": "Serra M, Alceste D, Hauser F, Hulshof PJM, Meijer HAJ, Thalheimer A, Steinert RE, Gero D, Bueter M, Gerber PA, Spector AC", "author_org": "University Hospital Zurich / University of Zurich; Wageningen University; University of Groningen; Florida State University", "publisher": "Frontiers in Nutrition 2023;10:1255499", "language": "en", "pub_date": "2023-09-22", "test_date": "8-day study period, 7 complete recording days (year not stated in extract)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10556674/", "archive_url": "", "sample_size": "30 adult women with normal BMI; 7 consecutive recording days each", "unit": "day", "reference_and_conditions": "Doubly labelled water (DLW) measured total energy expenditure as the reference for energy intake in weight-stable participants; 24-h recall (24HR) run as a second comparator. Free-living, self-photographed meals with a depth-sensing iPhone.", "original_metric": "mean daily energy intake, bias vs DLW (kcal/day), percentage difference, Bland-Altman limits of agreement", "original_numbers": "SNAQ 1905.5 +/- 531.1 kcal/day vs DLW 2235.2 +/- 456.5 kcal/day: bias -329.6 kcal/day, -12.8% (SD 25.3), LoA -1503.8 to +844.5 kcal/day, p=0.067, R2=0.27. 24HR 1692.1 +/- 632.5 kcal/day: bias -543.0 kcal/day, -22.9% (SD 29.5), LoA -1802.5 to +716.5, p=0.00059, R2=0.34.", "quote_or_location": "Results tables: SNAQ vs DLW bias -329.6 kcal/day, -12.8% (SD 25.3), LoA -1503.8 to 844.5 kcal/day", "registry_metric": "bias lower bound", "registry_low_percent": 12.8, "registry_high_percent": "", "range_type": "point", "recomputation_formula": "published mean percentage difference vs DLW (-12.8%); a mean bias, hence a LOWER BOUND on absolute error. For scale, the limits of agreement span -1503.8 to +844.5 kcal/day, i.e. -67% to +38% of the DLW mean.", "assumptions": "Unit is a DAY of free-living intake; under-reporting includes unlogged eating occasions as well as photo estimation error. The 25.3 SD on the percentage difference shows individual days ranged far beyond the mean.", "result_status": "lower bound", "commercial_ties": "confirmed", "ties_note": "\"SNAQ AG developed the customized version of the app, SNAQ Study, in exchange for financial compensation. RS is employed by DSM Nutritional Products. AS is a member of the scientific advisory board of Gila Therapeutics.\" Funded by the Swiss National Science Foundation (32003B_182309/1). The developer built the tested build and advised on camera choice, but did not run the analysis; independent: partly.", "limitations": "Only 30 women, all normal-BMI; DLW measures expenditure, not intake, so weight stability is assumed; wide limits of agreement mean a single day's estimate is nearly uninformative even though the group mean is close.", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-20-e1", "status": "registered_not_charted", "reason": "calorie error published only in kcal on a menu-lookup task; no reference mean, no percentage, and no photo-only portion estimation was ever tested end-to-end", "group": "A", "subtype": "peer-reviewed", "independent": "no", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Im2Calories: towards an automated mobile vision food diary", "product": "im2calories (restaurant-menu variant); comparators MenuMatch and the crowdsourcing app MealSnap", "product_version": "research prototype, 2015", "measured_by": "Myers A, Johnston N, Rathod V, Korattikara A, Gorban A, Silberman N, Guadarrama S, Papandreou G, Huang J, Murphy K", "author_org": "University of Maryland (first author) and Google", "publisher": "ICCV 2015 (IEEE International Conference on Computer Vision), pp. 1233-1241", "language": "en", "pub_date": "2015-12 (ICCV 2015)", "test_date": "2015 or earlier", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Meyers_Im2Calories_Towards_an_ICCV_2015_paper.pdf", "archive_url": "", "sample_size": "MenuMatch dataset: 646 images, 41 food items, 3 restaurants (5-fold cross-validation)", "unit": "meal", "reference_and_conditions": "MenuMatch ground-truth calorie value per image computed by an expert nutritionist, plus published per-item calorie content from each restaurant's menu. The system is told which restaurant the user is in and looks calories up from that menu — it does not estimate portion size from the image.", "original_metric": "mean error and mean absolute error in calories (Table 2)", "original_numbers": "Table 2 (kcal): mean error -37.3 +/- 3.9 (baseline = restaurant prior mean), -268.5 +/- 13.3 (MealSnap), -21.0 +/- 11.6 (MenuMatch), -31.90 +/- 28.10 (im2calories C-hat), -25.35 +/- 26.37 (im2calories C-bar). Mean absolute error: 239.9 +/- 1.4 (baseline); 330.9 +/- 11.0 (MealSnap); 232.0 +/- 7.2 (MenuMatch); 163.43 +/- 16.32 (C-hat); 152.95 +/- 15.61 (C-bar).", "quote_or_location": "Table 2, \"Errors in calorie estimation on the MenuMatch dataset\"; Section 7 (Calorie estimation): \"we have not yet been able to conduct an end-to-end test of our system outside of the restaurant setting.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "multiple results", "recomputation_formula": "not possible — the mean reference calorie value of the MenuMatch meals is not published, so the kcal errors cannot be turned into a percentage", "assumptions": "none", "result_status": "measured", "commercial_ties": "confirmed", "ties_note": "Nine of ten authors are Google employees (@google.com addresses; Kevin Murphy, kpmurphy@google.com, Google Research); first author Austin Myers at amyers@umd.edu. No COI section in the ICCV paper. Independent: no.", "limitations": "CORRECTION TO A WIDELY REPEATED CLAIM: the paper reports NO percentage calorie error and NO end-to-end test outside restaurants. Verbatim: \"we do not yet have a broad coverage nutritional database for prepared foods, and therefore we have not yet been able to conduct an end-to-end test of our system outside of the restaurant setting.\" The only calorie numbers are kcal errors on the menu-lookup task, where even the best method (152.95 kcal MAE) beats a constant per-restaurant baseline (239.9 kcal) only moderately, and the earlier MenuMatch system (232.0) \"barely beats the baseline approach of predicting the prior mean\". Volume estimation was evaluated separately on 11 rubber food replicas (NFood-3d), in millilitres, never converted to calories.", "reprint_chain": "Secondary sources widely attribute '20% mean absolute error' and '20-40% error on cafeteria trays' to this paper. NEITHER NUMBER IS IN THE PAPER — see limitations.", "number_origin": "this paper (im2calories and baseline rows); MenuMatch and MealSnap rows measured here on the MenuMatch dataset of Beijbom et al.", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-21-e1", "status": "registered_not_charted", "reason": "energy agreement reported as coefficient of variation and limits of agreement only; no MAPE/WAPE derivable", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Performance of the digital dietary assessment tool MyFoodRepo", "product": "MyFoodRepo (MFR), automated photo-based dietary app", "product_version": "2020-2021 version", "measured_by": "Zuppinger C, Taffe P, Burger G, Badran-Amstutz W, Niemi T, Cornuz C, Belle FN, Chatelan A, Paclet Lafaille M, Bochud M, Gonseth Nussle S", "author_org": "Unisante / Center for Primary Care and Public Health, Lausanne; University of Geneva", "publisher": "Nutrients 2021 (manuscript PDF hosted by Canton de Vaud)", "language": "en", "pub_date": "2021", "test_date": "not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.vd.ch/fileadmin/user_upload/themes/sante/Prevention/CPSLA/Performance_of_the_digital_dietary_assessment_tool_MyFoodRepo.pdf", "archive_url": "", "sample_size": "189 records", "unit": "meal", "reference_and_conditions": "Controlled reference values from weighed food diaries (WFD) treated as an unbiased gold standard; agreement assessed by linear regression with modelled heteroscedasticity and coefficient of variation, not by MAE/MAPE.", "original_metric": "coefficient of variation of app estimates at given true values; regression intercept; portion-size error", "original_numbers": "Energy: mean coefficient of variation 0.35 across all records (0.58 at the 25th percentile of true values, 0.45 at the median, 0.37 at the 75th percentile); regression y-intercept 113.3 kcal for energy, indicating overestimation of small meals and underestimation of large ones. Food identification: 98.0% +/- 1.5 of edible components identified. Mean portion size error 9.2% +/- 48.1, individual errors from -88.5% to over +200%. Fat CV 0.42, carbohydrate 0.31, protein 0.38 (systematic protein overestimation), fibre 0.58, alcohol 1.25.", "quote_or_location": "Abstract: \"Linear regression analysis showed wide limits of agreements between MFR and WFD methods to estimate energy, carbohydrates, fat, proteins, fiber and alcohol contents of all records... MFR mean portion size error was 9.2% +/-48.1\"; Table 8: energy mean coefficient of variation 0.35", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "multiple conditions", "recomputation_formula": "not convertible — a coefficient of variation of app estimates around true values is a dispersion measure, not a mean absolute percentage error; no MAE and no mean reference energy are published", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "none found on check", "ties_note": "\"Conflicts of Interest: The authors declare no conflict of interest.\" Funded by the CPSLA of the Canton of Vaud; \"The funder had no role in the study design, data collection and analysis, nor in the article writing.\" MyFoodRepo originates from EPFL's digital epidemiology group; no EPFL/MyFoodRepo author appears on this paper.", "limitations": "Near-perfect food IDENTIFICATION (98%) coexists with wide limits of agreement for energy — the paper is a clean demonstration that recognition accuracy does not transfer to calorie accuracy. Portion-size error had an SD of 48 percentage points around a 9.2% mean.", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-22-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "Keenoa", "chart_metric": "Minimum error, from average daily underestimation", "chart_low_percent": 15.6, "chart_high_percent": 19.5, "chart_is_lower_bound": "yes", "chart_is_claim": "no", "chart_note": "Reference diary 2006.3 kcal/day; the app's own entries 1615.3; after a dietitian's review 1693. At least 19.5% and 15.6% missing.", "chart_limit": "The reference is another diary, itself imperfect. The result includes logging behaviour and human review, not image estimation alone.", "title": "Validity and Usability of a Smartphone Image-Based Dietary Assessment App Compared to 3-Day Food Diaries in Assessing Dietary Intake Among Canadian Adults: Randomized Controlled Trial", "product": "Keenoa (image-assisted app with AI food recognition, output reviewed by a research dietitian)", "product_version": "2019-2020 version", "measured_by": "Ji Y, Plourde H, Bouzo V, Kilgour RD, Cohen TR", "author_org": "McGill University, School of Human Nutrition; Concordia University; University of British Columbia", "publisher": "JMIR mHealth and uHealth 2020;8(9):e16953", "language": "en", "pub_date": "2020-09-09", "test_date": "2019-2020", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7511869/", "archive_url": "", "sample_size": "72 participants completed both methods (102 recruited, 27 withdrew)", "unit": "day", "reference_and_conditions": "3-day pen-and-paper food diary (3DFD) as the comparator (not weighed food, not a biomarker). IMPORTANT: the app output was corrected by a research dietitian before analysis (\"Keenoa-dietitian\"), so this is image-ASSISTED, not automatic photo-to-calorie estimation.", "original_metric": "mean energy intake per method, mean difference (kcal), percentage difference, correlation", "original_numbers": "3DFD 2006.3 (SD 540.5) kcal/day vs Keenoa-dietitian 1693.0 (SD 593.2) kcal/day; mean difference -313.3 kcal/day, -15.6%; correlations r = .38 to .51; significant differences found for total energy, protein, carbohydrate, %fat, saturated fat, iron and potassium (P<.05)", "quote_or_location": "Results / Table: 3DFD 2006.3 (540.5) kcal vs Keenoa-dietitian 1693.0 (593.2) kcal, difference -313.3 kcal (-15.6%); \"Significant differences in total energy, protein, carbohydrates, % fat, saturated fatty acids, iron, and potassium were found between the 3DFD and Keenoa-dietitian data (P<.05).\"", "registry_metric": "bias lower bound", "registry_low_percent": 15.6, "registry_high_percent": "", "range_type": "point", "recomputation_formula": "lower_bound = 100 x 313.3 / 2006.3 = 15.6% (the paper's own figure); mean bias only, so absolute error is higher and the upper bound is open", "assumptions": "Unit is a day; the reference is another self-report instrument, so neither side is ground truth. Correlations of .38-.51 indicate weak day-level agreement.", "result_status": "lower bound", "commercial_ties": "none found on check", "ties_note": "\"Conflicts of Interest: None declared.\" Funded by the R. Howard Webster Foundation through the PERFORM Centre, Concordia University. Keenoa staff (Anne-Julie Tessier RD, Anthony Grant) are thanked for assistance with the app but are not authors and had no stated role in design or analysis.", "limitations": "Image-ASSISTED with dietitian correction, so it does not measure automatic photo-calorie accuracy; comparator is a self-reported diary rather than weighed food; high dropout (27/102).", "reprint_chain": "none traced", "number_origin": "this study", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "SCI-23-e1", "status": "registered_not_charted", "reason": "review contains essentially no calorie-error data of its own; the one figure is a 2011 crowdsourcing result outside this registry's window and product scope", "group": "A", "subtype": "review / meta-analysis", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Image-based food monitoring and dietary management for patients living with diabetes: a scoping review of calorie counting applications", "product": "14 image-based calorie-counting applications (aggregate)", "product_version": "n/a", "measured_by": "Rouhafzay A, Rouhafzay G, Jbilou J (review of 14 applications)", "author_org": "Universite de Moncton; University of Ottawa", "publisher": "Frontiers in Nutrition 2025;12:1501946", "language": "en", "pub_date": "2025-03-27", "test_date": "primary studies of varying dates", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2025.1501946/full", "archive_url": "", "sample_size": "14 applications reviewed", "unit": "dish", "reference_and_conditions": "Scoping review; reference methods vary by primary study.", "original_metric": "mostly food-classification accuracy and volume error; only one direct caloric error figure", "original_numbers": "PlateMate: +7.4% mean caloric overestimation, compared with +5.5% for a trained dietitian in the same study — primary citation Noronha et al. 2011 (PlateMate, UIST '11), a CROWDSOURCED (Mechanical Turk) system, not an AI app. GoCARB: volume MAPE 9.4% (volume, not energy). No other application in the review has a reported calorie error.", "quote_or_location": "Review results section: PlateMate mean caloric estimation error +7.4% vs trained dietitian +5.5% (citation [39] = Noronha et al. 2011); GoCARB volume MAPE 9.4%", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "n/a", "assumptions": "The single caloric figure belongs to a 2011 crowdsourcing system and predates the 2015-2026 window of this registry, so no separate record was created for it.", "result_status": "not recomputable", "commercial_ties": "none found on check", "ties_note": "\"The author(s) declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\" Funded by ResearchNB Strategic Initiative Grant and a PLEIADE Grant from Universite de Moncton.", "limitations": "USEFUL AS A NEGATIVE FINDING: a 2025 scoping review of 14 image-based calorie-counting apps for diabetes care could report only ONE direct calorie-error figure, and it came from a 2011 human-crowdsourcing system. Everything else in the literature it surveyed is recognition or volume accuracy.", "reprint_chain": "review reprinting one 2011 primary figure", "number_origin": "PlateMate figure originates in Noronha et al., UIST 2011", "coordinator_note": "Scoping review; contains no calorie-error datum inside the 2015-2026 window.", "second_pass_note": "" }, { "record_id": "app1-01-e1", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "app store page", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Foodvisor homepage (archived) — \"Key numbers\" section", "product": "Foodvisor (photo food logging app)", "product_version": "site of 2018; app launched Jan 2018", "measured_by": "n/a — no measurement published", "author_org": "Foodvisor (Fovea SAS, France)", "publisher": "foodvisor.io", "language": "en", "pub_date": "2018 (site version)", "test_date": "", "archive_date": "2018-03-22", "checked_date": "2026-09-05", "url": "https://foodvisor.io/", "archive_url": "https://web.archive.org/web/20180322091757/http://foodvisor.io/", "sample_size": "", "unit": "", "reference_and_conditions": "Marketing homepage. The only numbers offered are '2x more chances to reach your goal by keeping a food diary', '5 s to log a meal', '4.5/5 rating on the App Store'.", "original_metric": "none — no numeric accuracy claim", "original_numbers": "none found", "quote_or_location": "\"Key numbers / 2x more chances to reach your goal by keeping a food diary / 5 s to log a meal / 4.5/5 rating on the App Store\" (verbatim from archived page text)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Developer's own site.", "limitations": "Negative finding: through 2018-2019 Foodvisor published NO numeric accuracy figure. Launch-period press (FrenchWeb 2018-05-21, TechCrunch 2019-10-14) also carries no accuracy percentage.", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-02-e1", "status": "registered_not_charted", "reason": "food recognition accuracy, not calorie error (BRIEF §Percent scale rule)", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Food Image Recognition Explained (Foodvisor guides)", "product": "Foodvisor food recognition API (Foodvisor Vision) / same model as the app", "product_version": "API launched Oct 2023", "measured_by": "Foodvisor (no method, sample or protocol published)", "author_org": "Foodvisor", "publisher": "foodvisor.io", "language": "en", "pub_date": "2023-10-18", "test_date": "", "archive_date": "2023-12-10", "checked_date": "2026-09-05", "url": "https://www.foodvisor.io/en/guides/article/food-image-recognition-explained/", "archive_url": "https://web.archive.org/web/20231210054329/https://www.foodvisor.io/en/guides/article/food-image-recognition-explained/", "sample_size": "not stated (training set given as 10 million images; 20,000 foods)", "unit": "dish", "reference_and_conditions": "No reference standard, no test set, no protocol. Claim concerns FOOD IDENTIFICATION, not calories.", "original_metric": "'precision rate' / 'accuracy rate' of food recognition", "original_numbers": "95% precision; 'accuracy rate of over 95% on more than 20,000 foods'; '95 % accuracy'", "quote_or_location": "\"Trained on an extensive dataset of 10 million images, this API offers exceptional accuracy, recognizing 20,000 foods with an impressive 95% precision rate.\" and \"Accuracy: The Foodvisor food recognition API is highly accurate, with an accuracy rate of over 95% on more than 20,000 foods.\" and FAQ: \"Foodvisor food image recognition has a 95 % accuracy.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Published by the vendor to sell its Vision API.", "limitations": "Recognition accuracy, not calorie error. 'Precision' used loosely; no top-1/top-5 distinction, no dataset named. Independent measurement of the same product (Ji et al., PMC7752530) gives top-1 46.2% / top-5 71.5% — that source belongs to the group-A/editorial cluster, listed here only as context.", "reprint_chain": "No independent reprints of the 95% figure located in press; the figure lives only on Foodvisor properties (article + former vision.foodvisor.io docs).", "number_origin": "Foodvisor itself; no earlier appearance of the number found anywhere. publishedAt 2023-10-18T18:03:33Z per Foodvisor's own blog API; matches the 'Foodvisor Launches API' LinkedIn announcement of Oct 2023.", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-02-e2", "status": "registered_not_charted", "reason": "food recognition accuracy, not calorie error", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Foodvisor live properties, state on 2026-09-05", "product": "Foodvisor", "product_version": "live 2026-09-05", "measured_by": "n/a", "author_org": "Foodvisor", "publisher": "foodvisor.io / Apple App Store", "language": "en", "pub_date": "2026-09-05 (state as checked)", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.foodvisor.io/en/ ; https://apps.apple.com/us/app/foodvisor-nutrition-diet/id1096590231", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "Homepage and App Store listing checked today: no numeric accuracy claim. The 95% claim survives unchanged in the 2023 guides article (still live today). The API landing page https://www.foodvisor.io/en/vision/ now returns 404 and the docs host vision.foodvisor.io no longer resolves in DNS.", "original_metric": "unchanged 95% recognition claim on the guides article; nothing on homepage/App Store", "original_numbers": "95% (unchanged since 2023-10-18)", "quote_or_location": "Live guides article, sections 'Foodvisor groundbreaking food recognition API' and FAQ; homepage carries no percentage.", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "No change of the claimed figure was ever found: Foodvisor has published exactly one accuracy number, once, and has not revised it in ~3 years even though the product it advertised (Vision API) appears withdrawn.", "reprint_chain": "", "number_origin": "Foodvisor", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-03-e1", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Introducing Meal Scan (MyFitnessPal blog) — original launch text", "product": "MyFitnessPal Meal Scan (powered by Passio)", "product_version": "launch build, Dec 2020 (Premium, iOS/Android)", "measured_by": "n/a — no measurement published", "author_org": "MyFitnessPal, Inc.", "publisher": "blog.myfitnesspal.com", "language": "en", "pub_date": "2020-12-02 (page states 'Originally published December 2, 2020')", "test_date": "", "archive_date": "2021-03-25", "checked_date": "2026-09-05", "url": "https://blog.myfitnesspal.com/meal-scan/", "archive_url": "https://web.archive.org/web/20210325173421/https://blog.myfitnesspal.com/meal-scan/", "sample_size": "", "unit": "", "reference_and_conditions": "Launch announcement. Has a section literally headed 'BEST MATCH ACCURACY' that contains no number.", "original_metric": "none — no numeric accuracy claim", "original_numbers": "none found", "quote_or_location": "\"BEST MATCH ACCURACY — While accuracy improves with more frequent usage, the feature can already successfully identify thousands of foods. If it doesn't identify the right food, it'll offer a list of suggestions for you to approve.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Developer's own blog.", "limitations": "Negative finding verified on the archived 2021 snapshot, so the absence is historical and not the result of a later edit.", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-03-e2", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "press release", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "MyFitnessPal seasonal release press releases (2024 Summer, 2025 Winter, 2025 Summer, 2026 Winter)", "product": "MyFitnessPal Meal Scan", "product_version": "successive releases 2024-2026 (incl. iOS photo upload, 2026 Winter)", "measured_by": "n/a", "author_org": "MyFitnessPal, Inc.", "publisher": "PR Newswire", "language": "en", "pub_date": "2024-07-16 / 2025-02-25 / 2025-08-26 / 2026-02-24 (release dates as carried by PR Newswire and AOL wire)", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.prnewswire.com/news-releases/myfitnesspal-unveils-its-2024-summer-release-302196173.html ; https://www.prnewswire.com/news-releases/myfitnesspal-unveils-its-2025-winter-release-302385598.html ; https://www.prnewswire.com/news-releases/myfitnesspal-announces-its-2025-summer-release-302536319.html", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "All quantities in these releases are business/database scale (250 million users, 19 million foods), never accuracy.", "original_metric": "none — qualitative only ('more accurate detection', 'even smarter AI')", "original_numbers": "none found", "quote_or_location": "2025 Summer: \"Premium and Premium+ members gain access to an improved Meal Scan feature, powered by an even smarter AI.\" 2024 Summer: \"Meal Scan leverages artificial intelligence to identify the food items in a photo and instantly match them to the foods in MyFitnessPal's extensive database.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "MFP consistently frames improvement comparatively without a baseline or a figure.", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-03-e3", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "MyFitnessPal current Meal Scan claims (blog + support FAQ + App Store), state on 2026-09-05", "product": "MyFitnessPal Meal Scan (models now built and run by MyFitnessPal, no longer credited to Passio in the FAQ)", "product_version": "live 2026-09-05; FAQ names app version 22.17+", "measured_by": "n/a", "author_org": "MyFitnessPal, Inc.", "publisher": "blog.myfitnesspal.com / support.myfitnesspal.com / Apple App Store", "language": "en", "pub_date": "blog page last updated 2025-10-09", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://blog.myfitnesspal.com/meal-scan/ ; https://support.myfitnesspal.com/hc/en-us/articles/360045761612-Meal-Scan-FAQ", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "Checked today: no percentage anywhere in the blog page, the FAQ, or the App Store description.", "original_metric": "none", "original_numbers": "none found", "quote_or_location": "FAQ: \"Leveraging models built and run by MyFitnessPal that are trained on millions of images, it analyzes the images and suggests verified foods from our vast food database.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "MyFitnessPal has never published a numeric accuracy figure for Meal Scan, from Dec 2020 to today. Its supplier Passio also publishes no top-1 figure on its MyFitnessPal case-study page.", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-04-e1", "status": "registered_not_charted", "reason": "food recognition accuracy on a public image dataset, not calorie error", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lose It! Snap It launch fact sheet, quoted in full by MobiHealthNews", "product": "Lose It! Snap It (beta)", "product_version": "Snap It beta, Sept 2016", "measured_by": "Lose It! (self-reported classification benchmark)", "author_org": "FitNow, Inc. (Lose It!)", "publisher": "MobiHealthNews (quoting the company's launch fact sheet)", "language": "en", "pub_date": "2016-09-29", "test_date": "before 2016-09-29 (not stated)", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.mobihealthnews.com/news/lose-it-now-lets-users-log-foods-their-phones-camera-and-machine-learning-algorithm", "archive_url": "", "sample_size": "Food-101 test set (101 categories, 101,000 images in the dataset)", "unit": "dish", "reference_and_conditions": "Image-classification benchmark on the public Food-101 dataset; ground truth = dataset labels. Compared against the FoodDist model (Cornell/UCLA), stated at 83.1% top-1 / 95.8% top-5. NOT a calorie or portion measurement, and not a measurement on user photos.", "original_metric": "top-1 and top-5 classification accuracy on Food-101", "original_numbers": "87.3% top-1; 97.1% top-5 (baseline quoted: FoodDist 83.1% top-1, 95.8% top-5)", "quote_or_location": "\"FoodDist, the model developed by researchers at Cornell and UCLA, has, until now, held the highest prediction accuracy rate of 83.1 percent for top-1 accuracy and 95.8 percent for top-5 accuracy. Snap It's model improves upon that benchmark, by meeting an accuracy rate of 87.3 percent for top-1 accuracy and 97.1 percent for top-5 accuracy.\" (company fact sheet, quoted by MobiHealthNews)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "multiple results", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Number produced and published by the app developer; no independent replication, no code or model released.", "limitations": "Food-101 top-1/top-5 says nothing about calories, portions, or multi-food plates; the shipped feature was a beta that showed a candidate list for the user to pick from — which is precisely what top-5 flatters.", "reprint_chain": "Fact sheet -> MobiHealthNews 2016-09-29 (quotes it verbatim, attributes to 'a fact sheet about the launch') and Digital Trends (quotes verbatim, 'according to the company'); TechCrunch 2016-09-29 compresses the two figures into a misleading interval '87.3% to 97.1% accuracy ... within that data set'; Science World Report, HungryForever and others then repeat the TechCrunch/DT framing. Ten repetitions, one measurement.", "number_origin": "FitNow/Lose It! launch fact sheet (press kit), 2016-09-29 — this is the earliest verifiable numeric accuracy claim of any app in this cluster.", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-05-e1", "status": "registered_not_charted", "reason": "food recognition accuracy, not calorie error; also a reprint of app1-04", "group": "B", "subtype": "news reprint", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lose It launches Snap It to let users count calories in food photos (TechCrunch)", "product": "Lose It! Snap It (beta)", "product_version": "Snap It beta, Sept 2016", "measured_by": "Lose It! (reprinted)", "author_org": "TechCrunch", "publisher": "TechCrunch", "language": "en", "pub_date": "2016-09-29", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://techcrunch.com/2016/09/29/lose-it-launches-snap-it-to-let-users-count-calories-in-food-photos/", "archive_url": "", "sample_size": "Food-101", "unit": "dish", "reference_and_conditions": "Same numbers as app1-04, restated as a range without the top-1/top-5 distinction.", "original_metric": "'accuracy' (undifferentiated)", "original_numbers": "87.3% to 97.1%", "quote_or_location": "\"Lose It claims that Snap It beta achieves 87.3% to 97.1% accuracy already within that data set.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "reported spread", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "none found on check", "ties_note": "Author Lora Kolodny; no disclosed ties. Attributes the number to the company ('Lose It claims').", "limitations": "Presenting top-1 and top-5 as the endpoints of an accuracy range is a category error introduced at the reprint stage.", "reprint_chain": "This is the version most later pages copy, which is why '87.3-97.1% accuracy' circulates as if it were a range of measured outcomes rather than two different metrics on one dataset.", "number_origin": "Lose It! fact sheet (see app1-04)", "coordinator_note": "Also logged by cluster APP2 (dropped as duplicate). NVIDIA dev blog 2016-10-04 = app2-15.", "second_pass_note": "" }, { "record_id": "app1-06-e1", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "app store page", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "loseit.com/snapit landing page (archived)", "product": "Lose It! Snap It", "product_version": "2016-2018 site", "measured_by": "n/a", "author_org": "FitNow, Inc. (Lose It!)", "publisher": "loseit.com", "language": "en", "pub_date": "2016", "test_date": "", "archive_date": "2016-10-02 (also checked 2018-06-16)", "checked_date": "2026-09-05", "url": "https://www.loseit.com/snapit/", "archive_url": "https://web.archive.org/web/20161002154428/http://loseit.com/snapit/", "sample_size": "", "unit": "", "reference_and_conditions": "The consumer-facing page carries only superlatives; the numbers stayed in the press kit.", "original_metric": "none", "original_numbers": "none found", "quote_or_location": "\"The Science: a Best-in-Class Food Image Analysis Model ... Snap It is the new standard in food image recognition.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-07-e1", "status": "registered_not_charted", "reason": "no accuracy figure; weight-loss/engagement outcomes are a different quantity", "group": "B", "subtype": "press release", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lose It! Finds AI-Powered Logging Boosts Weight Loss Success and Greater Nutritional Mindfulness", "product": "Lose It! Snap It / Say It", "product_version": "'latest Snap It model' (identifies all foods of a meal in one snapshot)", "measured_by": "Lose It! internal member data (method not published)", "author_org": "FitNow, Inc. (Lose It!), part of Everyday Health Group / Ziff Davis", "publisher": "Newswire.com (also AccessNewswire, barchart, pr-inside)", "language": "en", "pub_date": "2025-04-22", "test_date": "not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.newswire.com/news/lose-it-finds-ai-powered-logging-boosts-weight-loss-success-and-22557702", "archive_url": "", "sample_size": "not stated (company cites 57M members, 63M-item food database)", "unit": "member / logging session", "reference_and_conditions": "Outcome and behaviour metrics, no accuracy metric, no control described.", "original_metric": "behavioural outcomes", "original_numbers": "6% More Weight Loss; 3.5x Faster Meal Logging; ~2x as many foods logged", "quote_or_location": "\"Our breakthrough technology combines visual recognition with advanced artificial intelligence to identify foods with remarkable speed and accuracy... This innovative approach has helped us achieve new levels of precision in automated food logging.\" (Will Lowe, PhD, Chief Data Scientist, FitNow)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "multiple results", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Company press release about its own members.", "limitations": "The word 'accurate' is used seven times with no figure attached: 'new levels of precision in automated food logging' is asserted, never quantified. This is the moment Lose It! stopped publishing a number while continuing to claim accuracy.", "reprint_chain": "Distributed via Newswire and syndicated verbatim (AccessNewswire, barchart, pr-inside).", "number_origin": "FitNow internal analytics", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-07-e2", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lose It! current Snap It claims (support article + App Store), state on 2026-09-05", "product": "Lose It! Snap It (Premium)", "product_version": "live 2026-09-05", "measured_by": "n/a", "author_org": "FitNow, Inc.", "publisher": "loseit.zendesk.com / Apple App Store", "language": "en", "pub_date": "2026-09-05 (state as checked)", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://loseit.zendesk.com/hc/en-us/articles/47771695186580-How-to-Use-Snap-It ; https://apps.apple.com/us/app/lose-it-calorie-counter/id297368629", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "Checked today: no percentage. Support page gives photographic hygiene tips 'to get the most accurate results'; App Store text says 'Quickly and accurately log healthy meals using your voice or a photo'.", "original_metric": "none", "original_numbers": "none found", "quote_or_location": "App Store: \"AI VOICE & PHOTO MEAL LOGGING Quickly and accurately log healthy meals using your voice or a photo.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "Chronology for Lose It!: a precise, checkable, dataset-anchored number in 2016 -> no number at all in 2025-2026, while the marketing adjective stayed.", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-08-e1", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "press release", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "YAZIO press factsheet, December 2025", "product": "Yazio AI food scanner (photo calorie tracking)", "product_version": "AI food tracking feature shipped June 2025; rebrand Oct 2025", "measured_by": "n/a", "author_org": "YAZIO GmbH (Erfurt, Germany)", "publisher": "yazio.com press area", "language": "en", "pub_date": "2025-12 (factsheet dated 'December 2025')", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://filecontent.yazio.com/press/factsheet-yazio-en.pdf", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "Company milestone sheet. Fixes the feature's birth date; contains no accuracy figure.", "original_metric": "none", "original_numbers": "none found (milestones only: '2025 June: AI food tracking feature'; 100M users)", "quote_or_location": "PDF text: \"2025 June: AI food tracking feature\"; \"innovative AI food scanner, a convenient barcode scanner, detailed analyses\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "German-language press search (t3n, Gründerszene) returned no YAZIO launch release for the AI feature at all; the feature was announced via the app and social channels.", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-08-e2", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "What is the AI Calorie Tracking Feature, and how does it work? (Yazio Help Center)", "product": "Yazio AI calorie tracking (photo)", "product_version": "feature moved from free-for-all to Yazio Pro", "measured_by": "n/a", "author_org": "YAZIO GmbH", "publisher": "help.yazio.com", "language": "en", "pub_date": "not stated on page", "test_date": "", "archive_date": "2025-10-09", "checked_date": "2026-09-05", "url": "https://help.yazio.com/hc/en-us/articles/39137901903889-What-is-the-AI-Calorie-Tracking-Feature-and-how-does-it-work", "archive_url": "https://web.archive.org/web/20251009051912/https://help.yazio.com/hc/en-us/articles/39137901903889-What-is-the-AI-Calorie-Tracking-Feature-and-how-does-it-work", "sample_size": "", "unit": "", "reference_and_conditions": "The only place YAZIO characterises accuracy — and it does so by disclaiming it. Claim existed no later than 2025-10-09 (archive snapshot); wording unchanged today.", "original_metric": "explicit non-claim", "original_numbers": "none — the sole percentage is the disclaimed '100%'", "quote_or_location": "\"Important: The AI recognition only provides a rough estimate and cannot guarantee 100% accurate results. However, the more you use our AI tracker, the better and more accurate it will become!\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-08-e3", "status": "registered_not_charted", "reason": "database coverage claim, not accuracy of any estimate", "group": "B", "subtype": "app store page", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "AI Calorie Tracker by Yazio — App Store description, state on 2026-09-05", "product": "Yazio", "product_version": "live listing 2026-09-05", "measured_by": "n/a", "author_org": "YAZIO GmbH", "publisher": "Apple App Store (US)", "language": "en", "pub_date": "2026-09-05 (state as checked)", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://apps.apple.com/us/app/yazio-calorie-counter-app/id946099227", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "The listing carries one percentage, and it is about database coverage, not recognition or calorie error. Trap for anyone harvesting '95%' claims mechanically — both Foodvisor and Yazio show a '95%', measuring entirely different things.", "original_metric": "food database coverage", "original_numbers": "'Huge food database with 95% of all food items'", "quote_or_location": "\"- Huge food database with 95% of all food items\" (App Store description, feature bullet list)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "Unfalsifiable as written ('all food items' is undefined).", "reprint_chain": "", "number_origin": "YAZIO marketing copy; no basis published for '95% of all food items'.", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-09-e1", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lifesum adds AI image recognition for food logging (launch, iOS)", "product": "Lifesum image recognition (photo food logging), iOS first", "product_version": "Jan 2018 release", "measured_by": "n/a", "author_org": "Lifesum AB (Stockholm)", "publisher": "9to5Mac (reporting the company announcement); also iMore, iPhone Life", "language": "en", "pub_date": "2018-01-08", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://9to5mac.com/2018/01/08/life-sum-image-recognition-ios/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "Launch coverage carrying the company's own description. Establishes that Lifesum shipped photo recognition seven years before its 2025 'world-first' multimodal relaunch.", "original_metric": "none", "original_numbers": "none found", "quote_or_location": "\"With the new image recognition tool, users can simply snap a photo of their meal and, using the latest machine learning technology, the app will automatically recognize the ingredients.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "not disclosed", "ties_note": "Standard launch coverage; no accuracy figure supplied by the company to quote.", "limitations": "", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-09-e2", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "press release", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lifesum Transforms Meal Tracking with AI-Powered Multimodal Tracker for Personalized Nutrition", "product": "Lifesum Multimodal Tracker (photo, voice, text, barcode)", "product_version": "rollout early 2025", "measured_by": "n/a", "author_org": "Lifesum AB", "publisher": "PR Newswire (Cision); mirrored on lifesum.com as 'world-first'", "language": "en", "pub_date": "2025-02-05", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.prnewswire.com/news-releases/lifesum-transforms-meal-tracking-with-ai-powered-multimodal-tracker-for-personalized-nutrition-302367832.html", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "Every number in the release is market-size ($1.8tn wellness market, $480bn US, $9tn by 2028); none is about the product's accuracy. 'Early data shows higher retention, goal completion, and strong user engagement' is given without a single figure.", "original_metric": "none", "original_numbers": "none found", "quote_or_location": "\"By leveraging advanced image recognition and natural language processing technology to overcome these barriers, the Multimodal Tracker helps individuals make healthier choices...\" (Lars Hansson, Chief Experience Officer)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Paid wire distribution by the company.", "limitations": "", "reprint_chain": "Syndicated to Yahoo Finance, Athletech News, NutritionInsight — all reprint the release; none adds a measurement.", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app1-09-e3", "status": "registered_not_charted", "reason": "no numeric result", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lifesum current claims (Help Center AI Tracking + App Store), state on 2026-09-05", "product": "Lifesum AI Tracking", "product_version": "live 2026-09-05", "measured_by": "n/a", "author_org": "Lifesum AB", "publisher": "help.lifesum.com / Apple App Store", "language": "en", "pub_date": "2026-09-05 (state as checked)", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://help.lifesum.com/en/article/multimodal-tracking-explained-ai-tracking-15sw7rk/ ; https://apps.apple.com/us/app/lifesum-food-tracker-fasting/id286906691", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "Checked today: no percentage. The help page has a section 'Important Notes on AI Accuracy' that lists degrading conditions only.", "original_metric": "none", "original_numbers": "none found", "quote_or_location": "Help Center, section 'Important Notes on AI Accuracy': \"Blurry or complex images may reduce accuracy\"; \"Vague descriptions (e.g., 'a salad') may give less precise results\".", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "", "limitations": "Lifesum has published photo recognition since 2018 and has never attached a number to it.", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-01-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "B", "subtype": "news reprint", "independent": "n/a (group B)", "chart_title": "SnapCalorie", "chart_metric": "Company claim, formula undisclosed", "chart_low_percent": 20.0, "chart_high_percent": 20.0, "chart_is_lower_bound": "no", "chart_is_claim": "yes", "chart_note": "A founder quoted by TechCrunch on 26 June 2023: average caloric error below 20%. The service then used depth sensing and human reviewers.", "chart_limit": "No formula and no independent check for the quoted figure. The later 15% fits inside this earlier 'below 20%'.", "title": "SnapCalorie taps AI to estimate the caloric content of food from photos", "product": "SnapCalorie", "product_version": "launch build, June 2023", "measured_by": "SnapCalorie (Wade Norris) — no independent measurement by TechCrunch", "author_org": "TechCrunch", "publisher": "TechCrunch", "language": "en", "pub_date": "2023-06-26", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://techcrunch.com/2023/06/26/snapcalorie-computer-vision-health-app-raises-3m/", "archive_url": "", "sample_size": "", "unit": "dish", "reference_and_conditions": "Not stated in the article. Company says depth sensor on supported devices plus human reviewers.", "original_metric": "average caloric error", "original_numbers": "under 20%", "quote_or_location": "\"On average, the team is able to reduce the caloric error to under 20%\" — attributed to Wade Norris", "registry_metric": "MAPE-like", "registry_low_percent": 20, "registry_high_percent": 20, "range_type": "point", "recomputation_formula": "", "assumptions": "\"under 20%\" is an upper bound stated by the founder; treated as a point with an open lower side.", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Number originates from the co-founder of the company being profiled; article is launch/funding coverage.", "limitations": "No sample, no ground-truth protocol, no error formula published. EARLIEST verifiable numeric SnapCalorie accuracy claim found.", "reprint_chain": "TechCrunch 2023-06-26 -> PetaPixel 2023-06-28, gadgetbytenepal, citybiz and later SEO review sites", "number_origin": "SnapCalorie co-founder Wade Norris", "coordinator_note": "Founder statement to TechCrunch; no dataset or definition given. \"under 20%\" = upper bound 20, lower open.", "second_pass_note": "" }, { "record_id": "app2-02-e1", "status": "registered_not_charted", "reason": "Qualitative claim, no number in the dated snapshots.", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie company profile (Y Combinator startup directory)", "product": "SnapCalorie", "product_version": "", "measured_by": "SnapCalorie", "author_org": "SnapCalorie / Perception Labs, Inc.", "publisher": "Y Combinator", "language": "en", "pub_date": "", "test_date": "", "archive_date": "2023-04-17", "checked_date": "2026-09-05", "url": "https://www.ycombinator.com/companies/snapcalorie", "archive_url": "https://web.archive.org/web/20230417040222/https://www.ycombinator.com/companies/snapcalorie", "sample_size": "", "unit": "", "reference_and_conditions": "None given.", "original_metric": "qualitative comparison to a nutritionist", "original_numbers": "no number in the 2023-04-17 and 2024-02-02 snapshots", "quote_or_location": "\"It takes seconds to use and is more accurate than a trained nutritionist. The team previously co-founded Google Lens, Cloud Vision API, and published a CVPR paper demonstrating the first algorithm to outperform a professional nutritionist\" (2023-04-17 snapshot)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Self-written company profile on its investor's directory.", "limitations": "Claim existed no later than 2023-04-17, i.e. BEFORE the TechCrunch launch piece, but carries no number. A 'twice the accuracy of a professional nutritionist' wording appears on the LIVE page (checked 2026-09-05); it is NOT in the 2023-04-17 or 2024-02-02 snapshots, so its introduction date is not established.", "reprint_chain": "n/a", "number_origin": "SnapCalorie", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-03-e1", "status": "registered_not_charted", "reason": "Ratio vs an undefined baseline; not an absolute calorie error.", "group": "B", "subtype": "app store page", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie AI Calorie Counter — Apple App Store listing (id1574239307)", "product": "SnapCalorie", "product_version": "", "measured_by": "SnapCalorie", "author_org": "Perception Labs, Inc.", "publisher": "Apple App Store", "language": "en", "pub_date": "", "test_date": "", "archive_date": "2024-10-09", "checked_date": "2026-09-05", "url": "https://apps.apple.com/us/app/snapcalorie-ai-calorie-counter/id1574239307", "archive_url": "https://web.archive.org/web/20241009171159/https://apps.apple.com/us/app/snapcalorie-ai-calorie-counter/id1574239307", "sample_size": "Nutrition5k test set referenced (5,000 dishes) but not tied to the ratio", "unit": "dish", "reference_and_conditions": "LiDAR depth-sensor volume scan vs unaided visual portion estimation. No ground-truth protocol given for the ratio itself.", "original_metric": "accuracy ratio vs visual portion estimation", "original_numbers": "\"approximately twice as accurate\"; also 4.7/5 from 656 ratings at that date", "quote_or_location": "\"Our photo calorie counter is approximately twice as accurate as visually estimating portion sizes.\" (2024-10-09 snapshot, FAQ block of listing)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor-authored store listing.", "limitations": "A relative multiplier about PORTION-SIZE estimation, not a calorie error. Baseline ('visually estimating portion sizes') undefined.", "reprint_chain": "Echoed later by SEO review sites as 'roughly twice as accurate as visual estimation'.", "number_origin": "SnapCalorie", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-04-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "SnapCalorie FAQ", "chart_metric": "Company claim, formula undisclosed", "chart_low_percent": 15.0, "chart_high_percent": 15.0, "chart_is_lower_bound": "no", "chart_is_claim": "yes", "chart_note": "The FAQ says about 15% mean caloric error. Same wording in the archive of 21 January 2025 and on the live page on 5 September 2026.", "chart_limit": "Date is when the archive captured the claim, not a test date. Nutrition5k is cited, but the formula, app version and any independent check are not.", "title": "FAQ — SnapCalorie ('How accurate is SnapCalorie?')", "product": "SnapCalorie", "product_version": "", "measured_by": "SnapCalorie", "author_org": "Perception Labs, Inc. (SnapCalorie)", "publisher": "snapcalorie.com", "language": "en", "pub_date": "claim existed no later than 2025-01-21 (earliest archive capture); live unchanged 2026-09-05", "test_date": "", "archive_date": "2025-01-21", "checked_date": "2026-09-05", "url": "https://www.snapcalorie.com/faq.html", "archive_url": "https://web.archive.org/web/20250121220505/https://www.snapcalorie.com/faq.html", "sample_size": "implied: Nutrition5k test set, 5,000 dishes with every ingredient weighed", "unit": "dish", "reference_and_conditions": "Company states the figure derives from its Nutrition5k work: 5,000 dishes, every ingredient weighed, hundreds of photos per dish, algorithm evaluated on a held-out test set. The FAQ does not state which split, which model version, or the error formula.", "original_metric": "mean caloric error", "original_numbers": "SnapCalorie 15%; comparators quoted in the same sentence: nutrition labels allowed 20% error, professional dietitians from a photo ~40% error, average users of other nutrition tracking apps 53% error", "quote_or_location": "\"SnapCalorie is on average around 15% mean caloric error (that's +/- 150 calories on a 1000 calorie dish). By comparison nutrition labels are allowed to be 20% error, professional dietitians estimating from a photo are around 40% error, and average users of other nutrition tracking apps are 53% error.\"", "registry_metric": "MAPE-like", "registry_low_percent": 15, "registry_high_percent": 15, "range_type": "point", "recomputation_formula": "", "assumptions": "'mean caloric error' is not defined as MAPE or WAPE on the page; the parenthetical '+/- 150 calories on a 1000 calorie dish' implies a per-dish relative error, i.e. MAPE-like.", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor's own FAQ. Cited backing paper is co-authored by the vendor's parent company.", "limitations": "Claim existed no later than 2025-01-21 (earliest snapshot of faq.html; no earlier capture of this URL exists in the Wayback CDX index). Text is IDENTICAL in the 2025-01-21 snapshot and on the live page checked 2026-09-05. The 40% and 53% comparator figures are asserted without citation. The cited paper (Nutrition5k, CVPR 2021) is a POINTER — see app2_leads.json; it is the SCI agent's object.", "reprint_chain": "snapcalorie.com/faq.html -> wellnesspulse.com, home-cooks.co.uk, macaron.im, nutriscan.app, nutrola.app, whatthefood.io, clinicalnutritionreport.com and other review/affiliate pages, all repeating the same four numbers verbatim. Ten repetitions, one source.", "number_origin": "SnapCalorie (this FAQ is the origin of the 15 / 20 / 40 / 53 quartet)", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-05-e1", "status": "registered_not_charted", "reason": "No number.", "group": "B", "subtype": "app store page", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie AI Calorie Counter — Apple App Store listing, current text", "product": "SnapCalorie", "product_version": "", "measured_by": "SnapCalorie", "author_org": "Perception Labs, Inc.", "publisher": "Apple App Store", "language": "en", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://apps.apple.com/us/app/snapcalorie-ai-calorie-counter/id1574239307", "archive_url": "", "sample_size": "Nutrition5k, 5,000 dishes (referenced)", "unit": "dish", "reference_and_conditions": "None stated.", "original_metric": "none (superlatives only)", "original_numbers": "no accuracy percentage; rating 4.7/5 from 6.5K ratings", "quote_or_location": "\"THE MOST ACCURATE CALORIE TRACKER, PERIOD\"; \"Backed by peer-reviewed research on thousands of measured dishes\"; \"The only calorie tracker with this level of technical precision.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor-authored store listing.", "limitations": "CHANGE OF CLAIM: the numeric 'approximately twice as accurate' of the 2024-10-09 listing is gone; the current listing is purely qualitative ('THE MOST ACCURATE CALORIE TRACKER, PERIOD'). Popularity marker: ratings grew 656 (2024-10-09) -> 6.5K (2026-09-05).", "reprint_chain": "n/a", "number_origin": "SnapCalorie", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-06-e1", "status": "registered_not_charted", "reason": "Undefined metric — cannot be converted to calorie error; '100 - accuracy' is not licensed because the definition is not published.", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Cal AI — 'How accurate is Cal AI?' section on calai.app", "product": "Cal AI", "product_version": "", "measured_by": "Cal AI", "author_org": "Cal AI (Viral Development LLC)", "publisher": "calai.app", "language": "en", "pub_date": "", "test_date": "", "archive_date": "2024-09-18", "checked_date": "2026-09-05", "url": "https://www.calai.app/", "archive_url": "https://web.archive.org/web/20240918144016/https://www.calai.app/", "sample_size": "", "unit": "", "reference_and_conditions": "None. No ground truth, no sample, no definition of 'accurate'.", "original_metric": "unspecified 'accuracy' of the image scanning feature", "original_numbers": "90%", "quote_or_location": "\"How accurate is Cal AI? Cal AI's image scanning feature is generally 90% accurate.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor's own marketing site.", "limitations": "EARLIEST verifiable instance: 2024-09-18 snapshot (earliest HTTP-200 capture of calai.app after the app's 2024 launch; no earlier capture exists). Still present 2024-11-02, 2025-01-16, 2025-05-01. GONE by the 2025-07-11 snapshot (site redesign). What the 90% measures is never defined — it is not stated to be a calorie error. NO study is cited anywhere.", "reprint_chain": "calai.app -> CNBC and a large body of review/affiliate posts ('the creator says the app has a 90% accuracy rate'); no independent measurement anywhere in the chain.", "number_origin": "Cal AI (calai.app)", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-07-e1", "status": "registered_not_charted", "reason": "Undefined metric.", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Cal AI FAQ — 'The app scanned my food totally wrong'", "product": "Cal AI", "product_version": "", "measured_by": "Cal AI", "author_org": "Cal AI", "publisher": "calai.app", "language": "en", "pub_date": "", "test_date": "", "archive_date": "2026-08-01", "checked_date": "2026-09-05", "url": "https://www.calai.app/", "archive_url": "https://web.archive.org/web/20260801201409/https://www.calai.app/", "sample_size": "", "unit": "", "reference_and_conditions": "None.", "original_metric": "unspecified 'accuracy'", "original_numbers": "about 80%", "quote_or_location": "\"CalAI is about 80% accurate. No food tracking app is perfect, and calories and nutrition values can vary.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor's own FAQ.", "limitations": "CHANGE OF CLAIM, DOWNWARD: absent from the 2026-06-09 and 2026-07-20 snapshots, present in the 2026-08-01 snapshot, so the 80% figure was introduced between 2026-07-20 and 2026-08-01. Confirmed live 2026-09-05. Same undefined metric as the 90% claim; the two numbers coexist in third-party writing because the site changed. Cal AI popularity (self-reported, same page): \"Loved by 5M users with 4.9 rating\".", "reprint_chain": "n/a (new)", "number_origin": "Cal AI", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-08-e1", "status": "registered_not_charted", "reason": "Database size, not an accuracy figure.", "group": "B", "subtype": "press release", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Introducing Calorie Mama", "product": "Calorie Mama (Azumio)", "product_version": "launch", "measured_by": "Azumio", "author_org": "Azumio Inc.", "publisher": "azumio.com blog", "language": "en", "pub_date": "2017-03-08", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.azumio.com/blog/azumio/calorie-mama-release", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "None.", "original_metric": "coverage (breadth), not accuracy", "original_numbers": "\"over one hundred thousand foods, packages and barcodes\"", "quote_or_location": "\"Our app can recognize over one hundred thousand foods, packages and barcodes.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor launch announcement.", "limitations": "NOT FOUND: no numeric accuracy claim in the 2017 launch announcement. The only number is a catalogue-size figure.", "reprint_chain": "Picked up by mhealthspot and app-listing sites.", "number_origin": "Azumio", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-09-e1", "status": "registered_not_charted", "reason": "No number published by the developer.", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Calorie Mama AI — Food Image Recognition (caloriemama.ai marketing site)", "product": "Calorie Mama (Azumio) / SNAP API, later 'Food AI API'", "product_version": "", "measured_by": "Azumio", "author_org": "Azumio Inc.", "publisher": "caloriemama.ai", "language": "en", "pub_date": "", "test_date": "", "archive_date": "2017-03-09 (earliest), 2018-01-11, 2021-10-19, 2024-11-14", "checked_date": "2026-09-05", "url": "https://www.caloriemama.ai/", "archive_url": "https://web.archive.org/web/20170309083010/http://www.caloriemama.ai/", "sample_size": "", "unit": "", "reference_and_conditions": "None.", "original_metric": "none", "original_numbers": "NONE — no accuracy percentage in any snapshot checked (2017-03-09, 2018-01-11, 2021-10-19, 2024-11-14)", "quote_or_location": "\"SNAP API accuracy constantly improves as new food images are added to the database.\" (2017-03-09 and 2018-01-11); rebranded but identical in substance in 2021-10-19 / 2024-11-14: \"Food AI API is based on the latest innovations in deep learning and image classification technology to quickly and accurately identify food items.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor site.", "limitations": "KEY NEGATIVE FINDING: across seven and a half years Azumio never published a numeric accuracy figure for Calorie Mama on its own marketing pages. The wording is unfalsifiable ('accuracy constantly improves'). The only widely quoted Calorie Mama number (63%) comes from an INDEPENDENT 2020 study, not from the vendor — see app2_leads.json.", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-10-e1", "status": "registered_not_charted", "reason": "No number.", "group": "B", "subtype": "news reprint", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Samsung Adds Food Image Recognition To Bixby Through Calorie Mama API", "product": "Samsung Bixby Vision food mode (powered by Calorie Mama API)", "product_version": "Galaxy S9 era", "measured_by": "nobody (press-release rewrite)", "author_org": "The Spoon", "publisher": "thespoon.tech", "language": "en", "pub_date": "2018-03-05", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://thespoon.tech/samsung-adds-food-image-recognition-to-bixby-through-calorie-mama-api/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "None.", "original_metric": "none", "original_numbers": "NONE", "quote_or_location": "\"Our vision for the Calorie Mama API is to provide the best food image recognition technology\" — Tom Xu, co-founder, Azumio", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Article is based on an Azumio/Samsung press release; only quote is from Azumio's co-founder.", "limitations": "Popularity anchor for the Calorie Mama engine (it shipped inside Bixby Vision on Samsung flagships), but carries NO accuracy number. Included because the brief named Samsung Health / Bixby Vision as a candidate: the answer is that no developer-origin numeric accuracy claim was found for it.", "reprint_chain": "Azumio/Samsung press release -> The Spoon", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-11-e1", "status": "registered_not_charted", "reason": "No accuracy number published.", "group": "B", "subtype": "press release", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Press — Bitesnap (official press kit)", "product": "BiteSnap (Bite AI)", "product_version": "launch (iPhone + Android, 2017)", "measured_by": "Bite AI", "author_org": "Bite AI, Inc.", "publisher": "getbitesnap.com", "language": "en", "pub_date": "", "test_date": "", "archive_date": "2017-05-17", "checked_date": "2026-09-05", "url": "https://getbitesnap.com/press/", "archive_url": "https://web.archive.org/web/20170517213900/http://getbitesnap.com/press/", "sample_size": "", "unit": "", "reference_and_conditions": "None.", "original_metric": "coverage (breadth), not accuracy", "original_numbers": "\"over 1,300 food and drink categories\"; obesity context figures (1 in 7 premature deaths, $147 billion)", "quote_or_location": "\"Bitesnap uses deep learning to identify over 1,300 food and drink categories.\" and \"Recent advances in deep learning have made it possible to reliably recognize objects with near human-level accuracy.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor press kit.", "limitations": "KEY NEGATIVE FINDING: no numeric accuracy claim. The phrase 'near human-level accuracy' is a statement about deep learning IN GENERAL as motivation, not a measured claim about BiteSnap. Live page unreachable on 2026-09-05 (expired TLS certificate) — the product is effectively defunct.", "reprint_chain": "Wording reused nearly verbatim by Android Police (2017-01-27) and Digital Trends.", "number_origin": "Bite AI", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-12-e1", "status": "registered_not_charted", "reason": "No number.", "group": "B", "subtype": "developer claim", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Bite AI — Food Intelligence Platform (corporate site)", "product": "Bite AI food recognition API (engine behind BiteSnap)", "product_version": "", "measured_by": "Bite AI", "author_org": "Bite AI, Inc.", "publisher": "bite.ai", "language": "en", "pub_date": "", "test_date": "", "archive_date": "2017-11-07 and 2018-04-24", "checked_date": "2026-09-05", "url": "https://bite.ai/", "archive_url": "https://web.archive.org/web/20171107083546/http://bite.ai/", "sample_size": "", "unit": "", "reference_and_conditions": "None.", "original_metric": "none", "original_numbers": "NONE", "quote_or_location": "\"We use state of the art machine learning algorithms to help computers understand and reason about food.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor site.", "limitations": "Identical text in the 2017-11-07 and 2018-04-24 snapshots; 'state of the art machine learning algorithms' with no metric. Live site unreachable 2026-09-05 (expired certificate).", "reprint_chain": "n/a", "number_origin": "n/a", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "app2-13-e1", "status": "registered_not_charted", "reason": "Hypothetical spoken remark ('maybe we get the calories off by 20 percent') about an unreleased research project; not a measured or claimed error of a shipped product.", "group": "B", "subtype": "news reprint", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Google's A.I. Is Training Itself To Count Calories In Food Photos", "product": "Google Im2Calories (research project; never shipped)", "product_version": "research prototype, 2015", "measured_by": "Google (Kevin Murphy) — no measurement by the publisher", "author_org": "Popular Science (Erik Sofge)", "publisher": "popsci.com", "language": "en", "pub_date": "2015-05-30", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.popsci.com/google-using-ai-count-calories-food-photos/", "archive_url": "", "sample_size": "", "unit": "dish", "reference_and_conditions": "None given in the interview; the number is spoken as a hypothetical tolerance, not a measurement.", "original_metric": "calorie error", "original_numbers": "\"off by 20 percent\"", "quote_or_location": "\"Ok fine, maybe we get the calories off by 20 percent. It doesn't matter. We're going to average over a week or a month or a year.\" — Kevin Murphy", "registry_metric": "MAPE-like", "registry_low_percent": 20, "registry_high_percent": 20, "range_type": "point", "recomputation_formula": "", "assumptions": "The speaker frames 20% as an acceptable-error hypothetical ('maybe we get the calories off by 20 percent'), NOT as a measured result. Charting it at all requires flagging it as rhetoric, not measurement.", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Speaker is the Google research scientist leading the project.", "limitations": "ADDED PRODUCT. Popularity evidence: not an app (never released) but the single most-reprinted photo-calorie announcement of the era — Google-authored and covered within days by Fortune, Smithsonian Magazine, CBC, Fox News, Popular Science, Digital Trends, ScienceAlert and a dozen more. It is the ORIGIN of the '20%' figure that later vendors echo. Companion peer-reviewed paper (ICCV 2015) belongs to the SCI agent — pointer in app2_leads.json.", "reprint_chain": "Popular Science 2015-05-30 (Erik Sofge) -> Fortune (2015-06-02), SiliconANGLE (06-03), Smithsonian, CBC, Search Engine Land, Digital Trends, ScienceAlert, TechXplore, Ubergizmo, WTKR, MobiHealthNews, Medical Daily, PC Tech Magazine, Techaeris (all within 2015-06-02..06-05). EARLIEST printed instance found: Popular Science.", "number_origin": "Kevin Murphy, Google, speaking at the Rework Deep Learning Summit (Boston, May 2015)", "coordinator_note": "Spoken hypothetical tolerance at a conference talk (\"maybe we get the calories off by 20 percent\"), not a measurement; the ICCV 2015 paper (SCI-20) contains no percentage. Kept on the chart as a developer statement, dashed/annotated.", "second_pass_note": "" }, { "record_id": "app2-15-e1", "status": "registered_not_charted", "reason": "No accuracy number.", "group": "B", "subtype": "news reprint", "independent": "n/a (group B)", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lose It!'s New Food Recognition App Counts Calories", "product": "Lose It! — Snap It (beta)", "product_version": "Snap It beta, 2016", "measured_by": "nobody", "author_org": "NVIDIA Developer Blog", "publisher": "developer.nvidia.com", "language": "en", "pub_date": "2016-10-04", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://developer.nvidia.com/blog/lose-its-new-food-recognition-app-counts-calories/", "archive_url": "", "sample_size": "230,000 food images; 4 billion foods logged since 2008", "unit": "", "reference_and_conditions": "None.", "original_metric": "none (training-data size only)", "original_numbers": "230,000 food images; more than 4 billion foods logged; four TITAN X GPUs", "quote_or_location": "goal \"to get the accuracy high enough in six months so it won't even need to ask you for validation\" — Edward W. Lowe; \"230,000 food images and more than 4 billion foods logged by Lose It! users since 2008\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "", "assumptions": "", "result_status": "developer claim", "commercial_ties": "confirmed", "ties_note": "Vendor-partner marketing blog (NVIDIA promoting its own GPUs and its customer).", "limitations": "No accuracy figure at all. Notable admission that accuracy was not yet sufficient: the stated goal was to reach accuracy high enough within six months that user validation would no longer be needed.", "reprint_chain": "NVIDIA blog -> NVIDIA developer forums repost", "number_origin": "Lose It! data scientist Edward W. Lowe", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-01-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "B", "subtype": "editorial test", "independent": "yes", "chart_title": "Tom’s Guide · Gemini", "chart_metric": "Error of the three-meal total, calculated bounds", "chart_low_percent": 5.3, "chart_high_percent": 27.2, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "References 175 / 295 / 328 kcal; Gemini answered with ranges 115–175 / 255–305 / 370–445. Taking the nearest and the farthest ends gives 5.3–27.2% of the total.", "chart_limit": "Photo plus a text description. Personal reference log, three meals. Bounds from answer ranges, not a measured average.", "title": "I hate paying for calorie-tracking apps, so I had Gemini analyze my food photos instead — here's what it found", "product": "Google Gemini (photo analysis of meals)", "product_version": "not stated (article says only 'Google Gemini'; no model version given)", "measured_by": "John Velasco, Senior Channel Editor for Phones, Tom's Guide (he weighed his own food and logged it)", "author_org": "Tom's Guide (Future plc)", "publisher": "Tom's Guide", "language": "en", "pub_date": "2026-07-16", "test_date": "not stated; article published 2026-07-16, experiment described as ongoing during author's 2026 fitness push", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.tomsguide.com/ai/google-gemini/i-hate-paying-for-calorie-tracking-apps-so-i-had-gemini-analyze-my-food-photos-instead-heres-what-it-found", "archive_url": "", "sample_size": "3", "unit": "dish", "reference_and_conditions": "Author's own weighed food log: ingredients weighed on a kitchen scale and logged (e.g. '276 grams of egg whites and one serving of chopped, steamed spinach'). Reference is a weighed-and-database-computed value, not a laboratory measurement. Photos were single flat/overhead phone photos of home-prepared meals; the author supplied verbal detail (e.g. 'egg whites rather than just eggs').", "original_metric": "per-dish calorie RANGE from Gemini vs author's weighed log total; no error formula stated by the author", "original_numbers": "Breakfast (egg whites + steamed spinach): Gemini 115-175 kcal, weighed log 175 kcal. Lunch (riced cauliflower + seasoned chicken breast): Gemini 255-305 kcal, weighed log 295 kcal. Dinner (bone-in chicken breast + honey-glazed carrots): Gemini 370-445 kcal, weighed log 328 kcal.", "quote_or_location": "\"It estimated the meal to be between 115 and 175 calories.\" / \"my own log of the meal was exactly 175 calories — consisting of 276 grams of egg whites and one serving of chopped, steamed spinach\" / \"Gemini analyzed the photo and estimated the meal to be between 255 and 305 calories. Once again, that's incredibly accurate, considering my own weighed-out log came in at 295 calories.\" / \"It ended up overestimating this meal, giving me a range of 370 to 445 calories when, in reality, it came out to just 328 calories.\"", "registry_metric": "WAPE", "registry_low_percent": 5.3, "registry_high_percent": 27.2, "range_type": "multiple conditions", "recomputation_formula": "Model returns intervals, so BRIEF 'computed bounds' rule applied. Per dish, min error = distance from reference to nearest interval edge, max error = distance to farthest edge. Breakfast (ref 175, interval 115-175): min 0, max 60. Lunch (ref 295, interval 255-305): min 0, max 40. Dinner (ref 328, interval 370-445): min 42, max 117. Sum of references = 798 kcal. WAPE_low = 100 x (0+0+42)/798 = 5.3%. WAPE_high = 100 x (60+40+117)/798 = 27.2%. Midpoint variant (if interval midpoints are taken as the estimate): errors 30, 15, 79.5 -> WAPE = 100 x 124.5/798 = 15.6%.", "assumptions": "These are computed bounds over the published intervals, NOT a confidence interval. Only 3 dishes; all home-prepared, low-to-mid calorie, single-plate meals. The author himself computes no error metric.", "result_status": "exact recomputation", "commercial_ties": "none found on check", "ties_note": "No affiliate widget or sponsored label on this article; Tom's Guide is an affiliate-revenue publisher generally, and the page carries a newsletter opt-in mentioning 'trusted partners or sponsors', but no product deal box or disclosure appears in this piece. Gemini is free, no purchase path.", "limitations": "n=3; single tester; no protocol, no repeat scans, no error formula; the 'true' values are the author's own weighed log (database-derived), so they carry the usual database/label error; the author explicitly notes the overhead dinner shot defeated Gemini's depth perception.", "reprint_chain": "none found; the lunch and dinner figures are echoed by AI search summaries but no independent reprint located", "number_origin": "Tom's Guide / John Velasco — original measurement, not reprinted", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-02-e1", "status": "charted", "reason": "On the chart. Chart values recomputed from the source's own numbers; see chart_note and recomputation_formula.", "group": "B", "subtype": "personal experiment", "independent": "no", "chart_title": "Kakao & Karotte · Lifesum", "chart_metric": "Error of the seven-meal total", "chart_low_percent": 34.6, "chart_high_percent": 34.6, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "Seven meals, all overestimated: 660 kcal too many out of 1905 reference, 34.6% of the total (WAPE); 41.9% average per meal.", "chart_limit": "The site's printed total of 1406 kcal does not match its own rows, which sum to 1905; we use the rows. Affiliate links on the page.", "title": "Kalorienzähler mit Foto – funktioniert das wirklich?", "product": "Lifesum (AI photo food logging)", "product_version": "not stated; tested over a 3-month period ending before publication 2025-05-16", "measured_by": "The blog authors — they photographed each dish with the app and, in parallel, weighed every ingredient to the gram and logged it in Yazio", "author_org": "Kakao & Karotte (private German food/health blog)", "publisher": "kakao-und-karotte.de", "language": "en", "pub_date": "2025-05-16", "test_date": "3-month test period ending shortly before 2025-05-16; exact dates not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://kakao-und-karotte.de/kalorienzaehler-mit-foto/", "archive_url": "", "sample_size": "7 dishes shown in the comparison table (the wider test ran 3 months)", "unit": "dish", "reference_and_conditions": "Own weighed reference: 'Parallel haben wir alles abgewogen und grammgenau mit Yazio getrackt' — every item weighed to the gram and computed in Yazio; for packaged items (e.g. frozen pizza) they cross-checked with the barcode/package data. So the reference is scale + nutrition database / package label. Real reference of stated quality; not another photo app.", "original_metric": "per-dish kcal from the Lifesum photo app vs gram-exact calculation, and the absolute difference in kcal ('Differenz'); no percentage or error formula stated by the authors", "original_numbers": "Pfannkuchen mit Emmentaler: app 384 kcal vs 342 kcal (+42). Pfannkuchen mit Mandelmus und Banane: 398 vs 362 (+36). Vollkorn-Apfelpfannkuchen: 135 vs 85 (+50). Pizzastueck Spinat Feta: 145 vs 121 (+24). Pizzastueck Mozzarella: 425 vs 201 (+224). Pizzastueck Grillgemuese: 471 vs 295 (+176). Kabeljau mit Gemuese und Gnocchi: 607 vs 499 (+108). Published 'Gesamtsumme' row: 2565 kcal vs 1406 kcal, '1159 kcal zu viel'. NOTE: the app column does sum to 2565, but the reference column of the same table sums to 1905, not 1406 — the published total row is internally inconsistent and was NOT used for the recomputation.", "quote_or_location": "\"Parallel haben wir alles abgewogen und grammgenau mit Yazio getrackt, damit wir einen Vergleich haben.\" / Table 'Gericht | Lifesum Foto App | Grammgenaue Berechnung | Differenz' in section 'Praxistest: Kalorienzaehlen mit Foto'.", "registry_metric": "MAPE", "registry_low_percent": 9.9, "registry_high_percent": 111.4, "range_type": "multiple results", "recomputation_formula": "Per-dish absolute percentage error = 100 x |app - reference| / reference: 12.3, 9.9, 58.8, 19.8, 111.4, 59.7, 21.6 %. Range low/high = min/max of these = 9.9 -> 111.4. Pooled MAPE (mean of the 7) = 41.9%. WAPE over the same 7 dishes = 100 x 660 / 1905 = 34.6%. All errors are OVERestimates by the app (systematic positive bias, +660 kcal over 7 dishes = +34.6% of reference).", "assumptions": "Recomputed from the published per-dish table, ignoring the inconsistent published total row. The seven tabled dishes are the only ones with numbers; the 3-month test is otherwise described qualitatively.", "result_status": "exact recomputation", "commercial_ties": "confirmed", "ties_note": "The page carries an explicitly labelled affiliate link and 20% discount code for YAZIO ('Offenlegung: -> Werbung (Affiliate Link)', commission disclosed). Yazio is ALSO the tool used to produce the gram-exact reference, and the tested photo app is a Yazio competitor (Lifesum). The authors state the products were self-chosen and self-bought and that the report is not influenced. Direction of the incentive is worth noting: the result is unfavourable to the competitor and favourable to the affiliate partner.", "limitations": "n=7 tabled dishes, one household, one app; three of the seven are portions of the same frozen pizza, where the authors explicitly say the app mistook a quarter/eighth of a pizza for a whole meal — i.e. a portion-fraction failure mode is over-represented. Published total row is arithmetically inconsistent with its own rows. Reference is scale + database, not lab.", "reprint_chain": "The ice-cream-cone figure (368 vs ~150 kcal) is echoed by German-language AI search summaries; no independent reprint of the table located.", "number_origin": "Kakao & Karotte — original measurement", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-02-e2", "status": "registered_not_charted", "reason": "The reference for the ice cream is the author's own estimate, not a weighed or labelled value.", "group": "B", "subtype": "personal experiment", "independent": "no", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Kalorienzähler mit Foto – funktioniert das wirklich? (single ice-cream-cone check)", "product": "Lifesum (AI photo food logging)", "product_version": "not stated", "measured_by": "The blog authors", "author_org": "Kakao & Karotte (private German food/health blog)", "publisher": "kakao-und-karotte.de", "language": "en", "pub_date": "2025-05-16", "test_date": "within the same 3-month test window", "archive_date": "", "checked_date": "2026-09-05", "url": "https://kakao-und-karotte.de/kalorienzaehler-mit-foto/", "archive_url": "", "sample_size": "1", "unit": "dish", "reference_and_conditions": "Estimated, NOT weighed: the authors reason that a German ice-cream-parlour scoop is ~50-70 g rather than the ~150 g of a US 'single scoop cone', and state the true value 'wohl etwa 150 kcal' ('probably about 150 kcal'). This is a reasoned estimate, weaker than the weighed table in e1. Kept as a separate experiment for that reason.", "original_metric": "single-dish app estimate vs authors' estimated true value; no error formula stated", "original_numbers": "Lifesum: 368 kcal for one scoop of ice cream in a cone; authors' stated true value ~150 kcal.", "quote_or_location": "\"So schlug die Lifesum App beim Bild meiner bescheidenen Eiskugel im Hoernchen satte 368 kcal vor, in Wirklichkeit waren es wohl etwa 150 kcal.\"", "registry_metric": "MAPE-like", "registry_low_percent": 145.3, "registry_high_percent": 145.3, "range_type": "point", "recomputation_formula": "100 x |368 - 150| / 150 = 145.3%", "assumptions": "Reference is the authors' own estimate ('wohl etwa 150 kcal'), not a weighing. Treat as illustrative of a locale/portion-prior failure mode rather than as a measurement.", "result_status": "estimated recomputation", "commercial_ties": "confirmed", "ties_note": "Same page, same disclosed YAZIO affiliate link.", "limitations": "n=1; reference is an estimate, not a measurement; explicitly framed by the authors as evidence that the model carries US portion priors.", "reprint_chain": "echoed in German AI search summaries", "number_origin": "Kakao & Karotte", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-03-e1", "status": "registered_not_charted", "reason": "No numeric calorie estimates or reference values are published; the result is a qualitative 'correct'. Not chartable.", "group": "B", "subtype": "editorial test", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "I used Apple's Visual Intelligence to count calories – here's how", "product": "Apple Visual Intelligence (iPhone 16 / Apple Intelligence, routing to ChatGPT via Siri)", "product_version": "not stated; Apple Intelligence Visual Intelligence as shipped in autumn 2024", "measured_by": "Tom's Guide staff writer (byline not exposed in the fetched body)", "author_org": "Tom's Guide (Future plc)", "publisher": "Tom's Guide", "language": "en", "pub_date": "2024-11-06", "test_date": "not stated; described as run in the run-up to the 2024 US holiday season", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.tomsguide.com/ai/apple-intelligence/i-used-apples-visual-intelligence-to-count-calories-heres-how", "archive_url": "", "sample_size": "4 named accuracy checks (Fritos snack bag, slice of bread, popsicle, boxed breakfast cereal) plus unquantified meals", "unit": "dish", "reference_and_conditions": "Package nutrition labels — 'the actual nutrition information on the back of the package'. That is a reference of stated quality (a label, with the usual regulatory rounding tolerance). Procedure: pour a snack-size bag into a bowl, photograph, compare to label.", "original_metric": "none — the outcome is reported only as 'correct' / 'accurate'; no error formula and no numbers", "original_numbers": "None published. Verdict text only: 'Siri gave me the correct amount of calories'; the bread, popsicle and cereal checks are described as 'each outcome was accurate'.", "quote_or_location": "\"I poured a snack sized bag of Fritos in a bowl. I then had Visual Intelligence analyze the image, which I compared to the actual nutrition information on the back of the package. I was pleasantly surprised that Siri gave me the correct amount of calories.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "not applicable — no numeric values published", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "none found on check", "ties_note": "No sponsorship or affiliate disclosure in the article body; a generic MacBook price-comparison widget appears in the page furniture (Future affiliate deals module), unrelated to the tested product.", "limitations": "The only rigorous part of the test is label-backed and packaged-food only; the meal part of the test required the author to tell Siri the quantity ('approximately half a cup'), so the portion estimate is the human's, not the model's. No numbers at all.", "reprint_chain": "none", "number_origin": "Tom's Guide — original hands-on, but no number produced", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-04-e1", "status": "registered_not_charted", "reason": "The prediction is not published — only the 190 kcal reference and a qualitative 'closer'. Not chartable.", "group": "B", "subtype": "editorial test", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "I tested Meta Ray-Bans vs Apple Visual Intelligence with 7 prompts — here's the winner", "product": "Ray-Ban Meta smart glasses (Meta AI) vs Apple Visual Intelligence", "product_version": "not stated", "measured_by": "Tom's Guide editor (Amanda Caswell / Tom's Guide AI desk)", "author_org": "Tom's Guide (Future plc)", "publisher": "Tom's Guide", "language": "en", "pub_date": "not extracted from page metadata (page live and dated only by section furniture); article checked 2026-09-05", "test_date": "not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.tomsguide.com/ai/i-tested-meta-ray-bans-vs-apple-visual-intelligence-with-7-prompts-heres-the-winner", "archive_url": "", "sample_size": "1 food prompt (a homemade muffin) inside a 7-prompt head-to-head", "unit": "dish", "reference_and_conditions": "The author's own recipe calculation: '190 calories per muffin based on the recipe'. A recipe-derived reference (ingredients known, not necessarily weighed on camera).", "original_metric": "qualitative 'closer to the real number'; no error formula", "original_numbers": "Reference: 190 kcal per muffin (from the recipe). Meta Ray-Bans: refused / could not give a calorie count. Apple Visual Intelligence: gave an estimate described as 'closer to the real number' but the estimate itself is NOT published.", "quote_or_location": "\"Although the calories were a rough estimate because it was a homemade muffin, the count was closer to the real number (190 calories per muffin based on the recipe).\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "not applicable — the model's own estimate is not published, only the reference", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "not disclosed", "ties_note": "Tom's Guide runs affiliate deal modules on smart-glasses coverage generally; no explicit sponsorship statement found on this page.", "limitations": "n=1 dish; the one number published is the reference, not the prediction. Also documents a refusal-to-answer failure mode (Meta AI declined to give a calorie count), which is not an error but a non-response.", "reprint_chain": "none", "number_origin": "Tom's Guide", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-05-e1", "status": "registered_not_charted", "reason": "No reference measurement exists; the two published numbers are predictions with nothing to compare them to.", "group": "B", "subtype": "editorial test", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "I swapped calorie-counting for a month with AI glasses — and finally hit my goal weight", "product": "Ray-Ban Meta Display smart glasses with Meta Muse Spark multimodal model", "product_version": "Meta's April 2026 update integrating the Muse Spark model", "measured_by": "Amanda Caswell, AI Editor, Tom's Guide", "author_org": "Tom's Guide (Future plc)", "publisher": "Tom's Guide", "language": "en", "pub_date": "2026-04-19", "test_date": "approximately one month, after Meta's April 2026 update; exact dates not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.tomsguide.com/ai/i-stopped-weighing-my-food-and-used-ray-ban-meta-glasses-to-track-calories-instead", "archive_url": "", "sample_size": "2 items with numbers (banana, handful of almonds); a month of unquantified use", "unit": "dish", "reference_and_conditions": "NO reference. The author explicitly stopped weighing food ('I stopped weighing my food'); no scale, label, menu or recipe value is given for any of the reported numbers. The claim that the glasses 'estimated the calories far more accurately than a standard flat photo ever could' is unsupported by any comparison.", "original_metric": "none", "original_numbers": "Glasses reported 105 kcal for a banana and 160 kcal for a handful of almonds. No true values given.", "quote_or_location": "\"A small overlay appeared in my line of sight: 105 calories for the banana, 160 for the almonds.\" / \"The glasses estimated the calories far more accurately than a standard flat photo ever could.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "not applicable — no reference values", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "not disclosed", "ties_note": "The article body carries an in-line product deal box ('Ray-Bans Ray-Ban Meta Display: $799 at Meta / View Deal'), i.e. a monetised purchase path for the device under test. No explicit sponsorship statement.", "limitations": "No ground truth of any kind; the accuracy claim is an impression. Included in the registry only to document that a widely-cited 'AI glasses beat flat photos' claim rests on no measurement.", "reprint_chain": "none", "number_origin": "Tom's Guide", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-06-e1", "status": "registered_not_charted", "reason": "Qualitative only — the article states no numeric error figures and does not name the apps.", "group": "B", "subtype": "editorial test", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "A UVA nutrition expert tested AI diet-tracking apps. Here's what she found", "product": "'several popular' AI diet-tracking apps — NOT NAMED in the article", "product_version": "not stated", "measured_by": "Sibylle Kranz, registered dietitian and nutrition researcher — hands-on trials with her own meals over weeks", "author_org": "University of Virginia (UVA Today); test run by Sibylle Kranz, RD, associate professor, UVA School of Education and Human Development", "publisher": "UVA Today (news.virginia.edu)", "language": "en", "pub_date": "2026-08-31", "test_date": "not stated; free trials, multiple user profiles, over a period described as including week-over-week re-logging", "archive_date": "", "checked_date": "2026-09-05", "url": "https://news.virginia.edu/content/uva-nutrition-expert-tested-ai-diet-tracking-apps-heres-what-she-found", "archive_url": "", "sample_size": "'dozens of examples' of misinterpreted photos; exact n not stated", "unit": "dish", "reference_and_conditions": "Her own knowledge of what she actually ate, as a registered dietitian, used to judge whether the app's identification was right; plus a repeat-logging consistency check (same dish logged a week apart). No scale-based reference reported, no calorie reference values.", "original_metric": "none — qualitative; explicitly no numeric error figures", "original_numbers": "None. Verdict quoted as 'a guessing game still' and 'The accuracy is just not there.'", "quote_or_location": "\"I could give you dozens of examples where I took a photo of something I ate, and then I had to correct it because it misinterpreted what's there... Although AI has come a long way, it's a guessing game still.\" / \"I took a photo of something it recognized, confirmed it, and put it in the database. A week later, I had the exact same thing, and it identified it differently or changed some of the ingredients... The accuracy is just not there.\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "not applicable", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "none found on check", "ties_note": "University press office publishing its own faculty member; no vendor named, no purchase path, no affiliate links. Kranz is an American Society for Nutrition Excellence in Nutrition Fellow.", "limitations": "Apps not named, so the finding cannot be attributed to any product; no numeric error; informal, unstructured protocol; the most concrete finding is a REPRODUCIBILITY failure (same dish, same app, different identification a week later), not a calorie-error magnitude.", "reprint_chain": "Widely reprinted: techxplore.com (2026-09), t3n.de 'Forscherin testet KI-Diaet-Apps' (2026-09-02), progressiverobot.com (2026-09-01), diningandcooking.com, newsbeep.com. All are downstream of this single UVA Today piece; none adds measurement.", "number_origin": "UVA Today / Kranz — original hands-on testing, no numbers produced", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-07-e1", "status": "registered_not_charted", "reason": "No reference and no numbers; the accuracy claim is unsupported by any measurement in the article.", "group": "B", "subtype": "editorial test", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "I let two AI apps track every bite I ate for three days straight to help me count calories and get back into shape. Here's the most accurate one", "product": "BitePal and SnapCalorie (AI photo food logging)", "product_version": "not stated", "measured_by": "Namerah Saud Fatmi, Android Central", "author_org": "Android Central (Future plc)", "publisher": "Android Central", "language": "en", "pub_date": "2026-08-05", "test_date": "three days; exact dates not stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.androidcentral.com/apps-software/ai/testing-calorie-counting-apps-for-three-days-this-one-is-more-accurate", "archive_url": "", "sample_size": "3 days of meals; n of dishes not stated", "unit": "day", "reference_and_conditions": "NONE. Despite the headline claim of 'the most accurate one', no scale, label, menu or recipe reference is used anywhere in the article. The accuracy verdict rests on the observation that SnapCalorie retrieved a specific cafe's coffee entry from the internet.", "original_metric": "none", "original_numbers": "None. Verdict text only: SnapCalorie 'much more accurate than BitePal'.", "quote_or_location": "\"Its AI food photo capture requires you to describe the food in a few words, which helps the AI analyze it more accurately and yield better results. It takes some time to process, but I found it much more accurate than BitePal.\" (section: comparison of BitePal and SnapCalorie)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "not applicable", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "not disclosed", "ties_note": "In-body Future affiliate deals module ('Today's best Google Pixel Watch 4, Fitbit Air... deals') unrelated to the apps; no sponsorship statement.", "limitations": "Headline asserts an accuracy ranking that the article never measures. Documented here mainly because it is being reprinted as evidence.", "reprint_chain": "Cited downstream by app-marketing blogs as 'Android Central rated SnapCalorie the most accurate photo estimator it tested' — a claim the article does not substantiate with any measurement.", "number_origin": "Android Central", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-08-e1", "status": "registered_not_charted", "reason": "Food-recognition impression and a body-weight outcome, not calorie error. Per BRIEF, recognition accuracy is not calorie error.", "group": "B", "subtype": "personal experiment", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "I Used ChatGPT as a Calorie Tracker, Did It Help Me Lose Weight?", "product": "ChatGPT (GPT-4o multimodal, free tier)", "product_version": "GPT-4o, free tier, as of May 2025", "measured_by": "Arjun Sha, Beebom — his own meals over ~2 years of use", "author_org": "Beebom", "publisher": "Beebom", "language": "en", "pub_date": "2025-05-07", "test_date": "ongoing personal use over roughly two years up to publication; no test window stated", "archive_date": "", "checked_date": "2026-09-05", "url": "https://beebom.com/i-used-chatgpt-as-calorie-tracker-to-lose-weight/", "archive_url": "", "sample_size": "not stated", "unit": "dish", "reference_and_conditions": "NONE for calories. The only quantified outcome is the author's body weight (105 kg -> 95 kg), which is an outcome measure, not a calorie-accuracy reference. The accuracy claim is limited to food identification ('even got the number of chapatis right').", "original_metric": "none", "original_numbers": "No calorie estimates or reference values published. Weight change: 10 kg (105 kg -> 95 kg).", "quote_or_location": "\"ChatGPT accurately identified the food items, and even got the number of chapatis right.\" / \"This focused approach to calorie tracking with ChatGPT has helped me reduce weight by 10 kg (22 lbs), from 105 kg (231 lbs) to 95 kg (209 lbs).\"", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "not applicable", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "none found on check", "ties_note": "No affiliate or sponsorship disclosure found; free-tier product, no purchase path promoted.", "limitations": "Weight loss is not evidence of calorie-estimation accuracy. The only accuracy statement concerns item recognition (counting chapatis), not energy. Kept in the registry as a documented example of the 'it works because I lost weight' inference.", "reprint_chain": "none", "number_origin": "Beebom", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-10-e1", "status": "charted", "reason": "On the chart. Published figure used as is.", "group": "B", "subtype": "editorial test", "independent": "no", "chart_title": "FeastGood", "chart_metric": "Error on one meal", "chart_low_percent": 0.7, "chart_high_percent": 11.8, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "One 440-kcal meal; three apps gave a total: 437, 470 and 388 kcal, that is 0.7%, 6.8% and 11.8% off. Three other apps gave no comparable total.", "chart_limit": "Default portion options were used for MyFitnessPal. Affiliate links disclosed. One successful meal says little about typical accuracy.", "title": "Testing The Meal Scan Accuracy For 6 Popular Nutrition Apps", "product": "SnapCalorie, MyNetDiary, MyFitnessPal, Foodvisor, Diet AI, FatSecret (photo 'Meal Scan' features)", "product_version": "not stated for any app; tested as of late 2024", "measured_by": "Colby Roy, Certified Nutrition Coach (Precision Nutrition); reviewed by Avi Silverberg, MSc. She cooked, weighed and logged the reference meals herself.", "author_org": "FeastGood.com", "publisher": "FeastGood.com", "language": "en", "pub_date": "2024-12-30", "test_date": "not stated; shortly before 2024-12-30", "archive_date": "", "checked_date": "2026-09-05", "url": "https://feastgood.com/meal-scan-accuracy-for-nutrition-apps/", "archive_url": "", "sample_size": "1 reference meal photographed into 6 apps; 3 apps returned a whole-meal calorie total", "unit": "dish", "reference_and_conditions": "Author's own weighed and logged meal: chicken breast + basmati rice + asparagus, stated reference 440 kcal / 12 g fat / 47 g carbs / 38 g protein. Reference is scale + database, of stated quality. Each app was additionally re-shot under varied lighting, camera angle (top-down vs 45 degrees) and plate design; the calorie figures below are from the good-lighting top-down condition.", "original_metric": "per-app whole-meal kcal vs the author's weighed reference; no error formula stated by the author", "original_numbers": "Reference meal 440 kcal. SnapCalorie 437 kcal (8 g fat, 43 C, 44 P). MyNetDiary 470 kcal (24 g fat vs actual 12 g). MyFitnessPal 388 kcal (7 g fat, 49 C, 30 P) before manual portion edits. Foodvisor: NO calorie total for the meal — it never detected the chicken or any protein; its component portions were rice 150 g vs actual 125 g and asparagus 60 g vs actual 75 g (mass, not kcal). Diet AI: misidentified the chicken as fish every time; gave rice 130 kcal against a stated 210 kcal, asparagus 20 kcal 'quite close'; portions editable only in whole 'servings'. FatSecret: suggested cabbage, popcorn, pasta with cheese, a ham-and-cheese sandwich; no portion estimate at all.", "quote_or_location": "\"It estimated my meal as having 437 calories, 8 grams of fat, 43 grams of carbs, and 44 grams of protein. The meal contained 440 calories, 12 grams of fat, 47 grams of carbs and 38 grams of protein.\" / \"It estimated the meal as having 470 calories (the actual meal was 440 calories), and in particular, over estimated the amount of fat (24 grams of fat vs the actual meal containing 12 grams).\" / \"the app calculated my meal as having 388 calories, 30 grams of protein, 49 grams of carbs, and 7 grams of fat.\"", "registry_metric": "MAPE", "registry_low_percent": 0.7, "registry_high_percent": 11.8, "range_type": "multiple results", "recomputation_formula": "Whole-meal absolute percentage error vs the 440 kcal reference: SnapCalorie |437-440|/440 = 0.7%; MyNetDiary |470-440|/440 = 6.8%; MyFitnessPal |388-440|/440 = 11.8%. Range = 0.7 -> 11.8. Pooled MAPE over the 3 apps that produced a total = 6.4%; WAPE over the same 3 = 100 x (3+30+52)/(440x3) = 6.4%.", "assumptions": "Only 3 of the 6 apps produced a whole-meal calorie number, so the range covers 3 apps, not 6 — the other 3 failed in ways that produce no error value at all (Foodvisor missed the protein entirely; FatSecret misidentified everything and estimated no portions; Diet AI misidentified the chicken as fish). Excluding them makes the range look far better than the test actually was; this is stated in limitations rather than fudged into the number. MyFitnessPal's figure comes from generic default portions the author selected from a suggestion list, not from photo-derived portions — the author says explicitly 'None of the portion sizes it suggested were based on the photo'.", "result_status": "exact recomputation", "commercial_ties": "confirmed", "ties_note": "Explicit disclosure at the top of the article: 'Some links in this article are affiliate links, which means we earn from qualifying purchases.' Every app named is linked, including the winner. FeastGood publishes paid app reviews of the same products. The winner (SnapCalorie) is not the most expensive or most prominent app, and the piece is broadly negative about 5 of 6, which cuts against a simple promotional read.", "limitations": "n=1 reference meal for the calorie comparison (plus one 'complex' oatmeal bowl, see e2); single household; no repeat-scan statistics reported for the calorie figures; app versions not recorded; three of six apps produce no comparable number, so the published range understates the category's failure rate. The most important qualitative finding is a non-response/misidentification failure mode rather than a calorie error.", "reprint_chain": "cited downstream by app-marketing blogs; no independent reprint of the numbers located", "number_origin": "FeastGood / Colby Roy — original measurement", "coordinator_note": "Single meal; 3 of 6 apps returned no total (failures not representable as an error value), so the range covers only the apps that produced a number.", "second_pass_note": "" }, { "record_id": "test-10-e2", "status": "registered_not_charted", "reason": "Component-level errors (oatmeal, toppings), not a meal result; the meal-level row test-10-e1 carries this source on the chart.", "group": "B", "subtype": "editorial test", "independent": "no", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Testing The Meal Scan Accuracy For 6 Popular Nutrition Apps (complex-meal / component-level estimates)", "product": "SnapCalorie, MyNetDiary, Diet AI (photo Meal Scan)", "product_version": "not stated", "measured_by": "Colby Roy, Certified Nutrition Coach", "author_org": "FeastGood.com", "publisher": "FeastGood.com", "language": "en", "pub_date": "2024-12-30", "test_date": "not stated; shortly before 2024-12-30", "archive_date": "", "checked_date": "2026-09-05", "url": "https://feastgood.com/meal-scan-accuracy-for-nutrition-apps/", "archive_url": "", "sample_size": "component-level estimates from the 'complex meal' (oatmeal packet cooked in water with vanilla protein powder, frozen blueberries, mini dark chocolate chips, Nutella), plus one component of the simple meal", "unit": "dish", "reference_and_conditions": "Author's weighed/known component values. Oatmeal component stated at 150 kcal; rice component of the simple meal stated at 210 kcal. Kept separate from e1 because these are single-COMPONENT errors, not whole-meal errors, and must not be averaged with e1.", "original_metric": "per-component kcal vs stated reference; no error formula", "original_numbers": "SnapCalorie oatmeal component: 187 kcal vs actual 150 kcal. MyNetDiary oatmeal component: 'overestimated the portion of oatmeal by about 220 calories' vs actual 150 kcal. Diet AI rice component: 130 kcal vs 210 kcal. Diet AI whole complex meal logged as one inseparable 'serving' at 450 kcal, 9 g protein, 65 g carbs, 20 g fat — no reference total published for the complex meal. Every one of the six apps missed the protein powder mixed into the oatmeal.", "quote_or_location": "\"The app estimated my oatmeal portion at 187 calories, and the actual portion was 150 calories.\" / \"it overestimated the portion of oatmeal by about 220 calories.\" / \"it's estimation for the calories in the rice portion was a bit low (130 calories when it should have been 210 calories)\"", "registry_metric": "MAPE-like", "registry_low_percent": 24.7, "registry_high_percent": 146.7, "range_type": "multiple results", "recomputation_formula": "SnapCalorie oatmeal |187-150|/150 = 24.7%. MyNetDiary oatmeal 220/150 = 146.7% (the author reports the error directly, 'about 220 calories' over a 150 kcal reference). Diet AI rice |130-210|/210 = 38.1%. Range 24.7 -> 146.7.", "assumptions": "MyNetDiary's 146.7% rests on the author's rounded phrase 'about 220 calories', so it is an approximate figure. These are component errors and are NOT comparable to whole-meal WAPE; charting them alongside meal-level numbers would overstate the spread.", "result_status": "estimated recomputation", "commercial_ties": "confirmed", "ties_note": "Same page, same affiliate disclosure as e1.", "limitations": "Component-level only; one dish; the 'about 220 calories' figure is rounded by the author; no complex-meal reference total is published, so no whole-meal error can be computed for the complex condition.", "reprint_chain": "none located", "number_origin": "FeastGood / Colby Roy", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "test-11-e1", "status": "registered_not_charted", "reason": "No calorie reference of any kind; findings are misidentification examples plus a body-weight outcome. Also a day-unit diary with self-reported, admitted under-logging.", "group": "B", "subtype": "personal experiment", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Kalorienzähl-Apps: Ich fotografiere mein Essen, damit Sie es nicht tun müssen", "product": "Yazio (photo logging), plus Garmin Connect 'Active Intelligence' nutrition logging and SnapCalorie shown in screenshots", "product_version": "not stated; Yazio one-month subscription taken for the test", "measured_by": "Handelsblatt reporter (self-experiment; byline not exposed in the archived body), photographing his own meals for 31 consecutive days", "author_org": "Handelsblatt", "publisher": "Handelsblatt", "language": "en", "pub_date": "2026-03-06 (Erstpublikation 06.03.2026, 04:00; page version dated 12.03.2026)", "test_date": "31 consecutive days ending shortly before first publication on 2026-03-06", "archive_date": "2026-03-12", "checked_date": "2026-09-05", "url": "https://www.handelsblatt.com/unternehmen/handel-konsumgueter/kalorienzaehl-apps-ich-fotografiere-mein-essen-damit-sie-es-nicht-tun-muessen-02/100204647.html", "archive_url": "https://web.archive.org/web/20260312140725/https://www.handelsblatt.com/unternehmen/handel-konsumgueter/kalorienzaehl-apps-ich-fotografiere-mein-essen-damit-sie-es-nicht-tun-muessen-02/100204647.html", "sample_size": "31 days of photographed meals; number of dishes not stated", "unit": "day", "reference_and_conditions": "NO calorie reference. Nothing was weighed and no label, menu or recipe value is given for any dish. The author judges the apps by whether they identified the food correctly, using his own knowledge of what he cooked and ate. The only quantified outcome is body weight (a real loss of 2.9% over the month).", "original_metric": "none — qualitative misidentification catalogue plus a body-weight outcome", "original_numbers": "One app output figure is quoted with no reference: ham-and-cheese sandwich with mayonnaise and cucumber, 513 kcal. Misidentifications logged: Turkish manti in yoghurt sauce read as Maultaschen with sour cream (and the roughly five tablespoons of butter on the manti dropped entirely); wheat sourdough bread persistently read as rye bread; a covered apple cake read as puff pastry cake with custard; puntarelle read as asparagus; almonds read as cashews; yoghurt over a nut mix ignored; hummus read as quark; grapefruit read as oranges; yoghurt with nuts read as pear pieces. Outcome: body weight -2.9%.", "quote_or_location": "\"An 31 Tagen habe ich mein Essen fotografiert und Kalorien addiert.\" / \"Aus den tuerkischen Teigtaschen, Manti, in Joghurtsauce werden Maultaschen mit Sauerrahm. Das ist - zumindest kalorisch - eine Liga, wenngleich bei den vermeintlichen Maultaschen die gut fuenf Essloeffel Butter auf den Manti unterschlagen werden.\" / \"So knipse ich mich mit gelegentlichen Ungenauigkeiten bis schlichten Betruegereien 31 Tage durch meinen Konsum.\" (Full text recovered from the 2026-03-12 Wayback snapshot, which captured the article past the paywall.)", "registry_metric": "none", "registry_low_percent": "", "registry_high_percent": "", "range_type": "point", "recomputation_formula": "not applicable — no reference calorie values published", "assumptions": "none", "result_status": "not recomputable", "commercial_ties": "none found on check", "ties_note": "Subscription paywalled news feature; the author states he bought the Yazio subscription himself. No affiliate links or sponsorship in the body; 'Unsere Partner' advertorial tiles in the page furniture are unrelated to the apps.", "limitations": "No ground truth, so no calorie error can be derived. Its real contribution is a different kind of evidence, and an unusually honest one: the author documents that HE corrupted the log on purpose — he omitted a bag of crisps, a glass of wine and a layer of Toffifee ('verschweige gaenzlich die Tuete Chips... ignoriere gaenzlich das Glas Wein'). That is diary error, not model error, and it is exactly the confound the BRIEF flags for day-level results. The 2.9% weight loss is an outcome, not an accuracy measure.", "reprint_chain": "none located", "number_origin": "Handelsblatt — original self-experiment, no accuracy number produced", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "cat-bayern1-2026", "status": "charted", "reason": "On the chart as an open-ended range: 6% to more than 20%; the upper end and the formula are unknown. Newsroom test, no complete dataset.", "group": "B", "subtype": "editorial test", "independent": "yes", "chart_title": "Bayern 1", "chart_metric": "Weekly deviation, formula undisclosed", "chart_low_percent": 6, "chart_high_percent": 20, "chart_is_lower_bound": "no", "chart_is_claim": "no", "chart_note": "Foodvisor was reported about 6% off across a week; deviations above 20% were reported for the others. The upper end is unknown.", "chart_limit": "Newsroom test of four apps without a full reference log or error formula. A weekly total can hide opposite errors on single meals. Page date 3 June 2026; metadata 12 June.", "title": "Kalorien zählen mit App: Wie gut erkennt die KI, was ich auf dem Teller habe?", "product": "Foodvisor, Yazio, Lifesum, Fddb (photo logging)", "product_version": "", "measured_by": "Bayern 1 Verbraucherredaktion (test explained by Nina Rathfelder)", "author_org": "", "publisher": "Bayerischer Rundfunk, Bayern 1", "language": "de", "pub_date": "2026-06-03", "test_date": "one week before publication; page 'Stand' 3 June 2026, metadata 12 June 2026", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.br.de/radio/bayern1/app-kalorien-zaehlen-100.html", "archive_url": "", "sample_size": "one week of meals, four apps", "unit": "week", "reference_and_conditions": "Reporter's own weighed/logged week; reference method and error formula not published.", "original_metric": "reported weekly deviation", "original_numbers": "Foodvisor about 6% off across the week; deviations above 20% reported for others", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "cat-serra-2026", "status": "catalogued_not_charted", "reason": "Diary-level result against energy expenditure: it mixes missed records and energy-balance assumptions with image error, so it is not charted as photo-estimation error. Kept in the catalogue.", "group": "A", "subtype": "peer-reviewed", "independent": "no", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Limited validity of an AI-powered app for dietary assessment in females with obesity", "product": "SNAQ (customized research build)", "product_version": "", "measured_by": "Serra M, Alceste D, Jucker N et al. (University Hospital Zurich)", "author_org": "", "publisher": "npj Digital Medicine", "language": "en", "pub_date": "2026-03-17", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://doi.org/10.1038/s41746-026-02536-2", "archive_url": "", "sample_size": "20 women, seven days", "unit": "day", "reference_and_conditions": "Doubly labelled water energy expenditure as the intake reference.", "original_metric": "mean bias and limits of agreement, kcal/day", "original_numbers": "25% underestimation; bias -817 kcal/day; limits of agreement -3707 to +2073 kcal/day", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "SNAQ AG paid to develop the customized app; author affiliations disclosed", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "cat-ji-2025", "status": "catalogued_not_charted", "reason": "Verified at abstract level only; no calorie-error percentage available to chart.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Using One-Shot Prompting of Non-Fine-Tuned Commercial Artificial Intelligence to Assess Nutrients from Photographs of Japanese Meals", "product": "GPT-4o", "product_version": "", "measured_by": "Ji Y, Waki K, Yamauchi T, Nangaku M, Ohe K", "author_org": "", "publisher": "Journal of Diabetes Science and Technology", "language": "en", "pub_date": "2025-01-10", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pubmed.ncbi.nlm.nih.gov/39791572/", "archive_url": "", "sample_size": "22 meals", "unit": "meal", "reference_and_conditions": "Weighed food records and dietitian comparison.", "original_metric": "agreement statistics per nutrient (abstract level)", "original_numbers": "poor overall performance; fiber better than other nutrients", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "cat-cinar-2026", "status": "catalogued_not_charted", "reason": "Full text not independently checked; only the qualitative finding is used (hidden fat, complex meals, benefit of context).", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Image-based nutritional assessment: Evaluating the performance of ChatGPT-4o on simple and complex meals", "product": "ChatGPT-4o", "product_version": "", "measured_by": "Cinar EN, Ozler E, Arslan S, Yilmaz S", "author_org": "", "publisher": "Journal of Food Composition and Analysis", "language": "en", "pub_date": "2025-12-27", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://doi.org/10.1016/j.jfca.2025.108843", "archive_url": "", "sample_size": "exploratory pilot, meals of differing complexity", "unit": "meal", "reference_and_conditions": "", "original_metric": "deviation from reference, qualitative summary in abstract", "original_numbers": "larger deviations for complex meals and visually obscured fat; improvement after contextual input", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "cat-vedovelli-2026", "status": "catalogued_not_charted", "reason": "RMSLE is a logarithmic error and does not convert to a calorie-error percentage.", "group": "A", "subtype": "peer-reviewed", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Model architecture dominates nutritional estimation accuracy in vision-language systems", "product": "40 vision-language models, eight providers", "product_version": "", "measured_by": "Vedovelli L, Pugnaloni S, Lanera C et al.", "author_org": "", "publisher": "Scientific Reports", "language": "en", "pub_date": "2026-06-25", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.nature.com/articles/s41598-026-58755-w", "archive_url": "", "sample_size": "Nutrition5k and original images; 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no photo-AI measurement.", "group": "B", "subtype": "editorial roundup", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "5 applications gratuites pour compter les calories", "product": "", "product_version": "", "measured_by": "Geoffroy Ondet", "author_org": "", "publisher": "01net", "language": "fr", "pub_date": "2025-12-06", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.01net.com/dossiers/5-applications-gratuites-pour-compter-les-calories-1423135.html", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "cat-fortune-roundup", "status": "rejected", "reason": "General buying guide without a specific photo test; not accuracy evidence.", "group": "B", "subtype": "buying guide", "independent": "yes", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Best calorie counter apps (roundup)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "Fortune", "language": "en", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://fortune.com/article/best-calorie-counter-apps/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-01", "status": "rejected", "reason": "FABRICATED SOURCE. Opened and read in full. Presents itself as a peer-reviewed study (authors 'Helena Weiss PhD MPH RD' et al., 'DOI 10.5281/zenodo.dai-2026-01', MAPE 1.1% for 'PlateLens' photo mode, 11.2% MyFitnessPal). The DOI is malformed and unresolvable, no journal, no indexing in PubMed/Europe PMC, the 'Dietary Assessment Initiative' has no institutional existence, and the top-ranked product 'PlateLens' does not exist in any independent literature. Appears to be AI-generated SEO material promoting 'PlateLens'. Do not cite, and treat its numbers as invented.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Independent validation of six commercial AI-assisted dietary assessment applications against weighed-food reference: a 180-meal cross-sectional study", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://dietaryassessmentinitiative.org/publications/six-app-validation-study-2026/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-02", "status": "rejected", "reason": "FABRICATED SOURCE, same network as the above. Claims monthly benchmark snapshots (PlateLens ±1.1%, Foodvisor ±5.3%, Bitesnap ±8.5%) but shows no commit history, no result JSON files, no named maintainers or affiliations. 'PlateLens' again leads. Not a real benchmark.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "foodvision-bench — 'Open reproducible benchmarks for food-image recognition models and APIs'", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://github.com/foodvision-bench/foodvision-bench", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-03", "status": "rejected", "reason": "Rejected without full extraction as part of the same 'PlateLens' SEO cluster: it sources its numbers to the fabricated 'DAI Six-App Validation Study (DAI-VAL-2026-01)'. Any 'SnapCalorie ±19.8% MAPE' figure traced to it is not a real measurement.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie vs PlateLens vs Cronometer: Accuracy Under Three Protocols (2026)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://clinicalnutritionreport.com/articles/snapcalorie-vs-platelens-vs-cronometer-accuracy-protocols-2026/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-04", "status": "rejected", "reason": "Commercial site presenting itself as a systematic review; not opened as a primary source and not independently verifiable. Excluded from cluster A (would at best be group B editorial).", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "The Evidence Base for AI Nutrition Accuracy: A Systematic Review (2026)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.nutrientmetrics.com/en/guides/peer-reviewed-ai-nutrition-accuracy-literature-review", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-05", "status": "rejected", "reason": "Primary paper opened but its numeric results could NOT be extracted (PDF text extraction failed on the results section; the CVF/openaccess HTML mirror and Semantic Scholar returned 403/empty). Secondary sources claim '20% mean absolute error on constrained restaurant meals' and '20-40% on cafeteria trays', but neither number was confirmed in the primary text, so no record was written. NOT FOUND as verified — flag for a re-try with a PDF text extractor.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Im2Calories: Towards an Automated Mobile Vision Food Diary (Meyers et al., ICCV 2015)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Meyers_Im2Calories_Towards_an_ICCV_2015_paper.pdf", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-06", "status": "rejected", "reason": "HTTP 403 to WebFetch. Record SCI-16 was built instead from the Europe PMC abstract record (PMID 32839035, doi 10.1016/j.clnu.2020.08.002); funding/COI text remains unverified.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Validity of image-based dietary assessment methods: A systematic review and meta-analysis (Elsevier landing page)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.sciencedirect.com/science/article/abs/pii/S0261561420304039", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-07", "status": "rejected", "reason": "HTTP 403. Also note this URL's DOI (10.1016/j.ajcnut.2025.09.016) resolves to an unrelated pesticide-exposure paper; the correct Diabot record is doi 10.1016/j.ajcnut.2025.10.013 / PMID 41138916, used for SCI-12.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Customized multimodal Diabot-GPT-4o ... (Elsevier landing page)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.sciencedirect.com/science/article/abs/pii/S0002916525006173", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-08", "status": "rejected", "reason": "MDPI returned HTTP 403 to both WebFetch and curl; no PMC deposit. Only the Europe PMC abstract was usable, so SCI-11 is recorded with no chart value.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Prompt Engineering and Model Selection for LLM-Based Nutritional Estimation from Food Images", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.mdpi.com/2072-6643/18/12/2017", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-09", "status": "rejected", "reason": "HTTP 403; duplicate of the open PMC version (PMC10836267) used for SCI-15.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "AI-based digital image dietary assessment methods compared to humans and ground truth (publisher page)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.tandfonline.com/doi/full/10.1080/07853890.2023.2273497", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-10", "status": "rejected", "reason": "News reprint of the NIDDK/NUTRITION 2026 press release (group B, not this cluster). Adds no measurement; kept only as part of the reprint chain of SCI-14.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Photo-based calorie-tracking apps fall short in estimating meal nutrition", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.news-medical.net/news/20260726/Photo-based-calorie-tracking-apps-fall-short-in-estimating-meal-nutrition.aspx", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-11", "status": "rejected", "reason": "German-language reprint of the same NIDDK press release; no independent German measurement. Group B, listed to document that the German-language search surfaced only reprints.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Kalorien-Tracking: Fotobasierte Apps verschätzen sich deutlich", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.pharmazeutische-zeitung.de/fotobasierte-apps-verschaetzen-sich-deutlich-167119/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-12", "status": "rejected", "reason": "Host refused the connection (ECONNREFUSED); the Springer version is paywalled. Numbers not verified, so no record written. The same authors' peer-reviewed Nutrients paper (SCI-08) covers the same ground and was used instead.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "CalorieVoL: Integrating Volumetric Context into Multimodal Large Language Models for Image-Based Calorie Estimation", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://mm.cs.uec.ac.jp/e/pub/conf24/250109tanabe_0.pdf", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-13", "status": "rejected", "reason": "PubMed returned only a cookie notice to WebFetch; superseded by the open PMC full text (PMC12513282) used for SCI-06.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Performance Evaluation of 3 Large Language Models... (PubMed page)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pubmed.ncbi.nlm.nih.gov/41081011/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-14", "status": "rejected", "reason": "SECOND PASS: PDF re-downloaded and extracted with pdftotext; Table 2 recovered and recorded as SCI-20. The earlier rejection is superseded. Key correction for the registry: the paper contains NO percentage calorie error and NO end-to-end test outside restaurants ('we have not yet been able to conduct an end-to-end test of our system outside of the restaurant setting'). The circulating claims of '20% mean absolute error' and '20-40% on cafeteria trays' are NOT in the primary paper.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Im2Calories (second pass) — RESOLVED, no longer rejected", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Meyers_Im2Calories_Towards_an_ICCV_2015_paper.pdf", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-15", "status": "rejected", "reason": "Out of scope and out of window: 2011, and the estimator is Amazon Mechanical Turk workers, not an app or model. It is the ONLY calorie-error figure (+7.4%) that the 2025 Frontiers scoping review (SCI-23) could report, which is itself recorded as a finding there. Not opened as a primary; no separate record created.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "PlateMate: Crowdsourcing Nutrition Analysis from Food Photographs (Noronha, Hysen, Zhang, Gajos, UIST 2011)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://iis.seas.harvard.edu/papers/2011/noronha-platemate-uist11.pdf", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-16", "status": "rejected", "reason": "Second pass: another domain in the fabricated 'PlateLens' cluster (cites 'DAI six-app benchmark', 'PlateLens ±1.4% MAPE', 'Foodvisor ±7.8%'). Sibling of nutrientmetrics.com already rejected. Numbers invented; do not cite.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "The best AI calorie tracker, 2026 | Nutrient Metrics", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://nutrient-metrics.com/en/guides/best-ai-calorie-tracker-2026/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-17", "status": "rejected", "reason": "Second pass: commercial listicle surfacing in the same result set as the fabricated PlateLens cluster; group B at best, no primary measurement. Not opened as a source of numbers.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Photo Calorie Counter Apps in 2026: How They Work and Which Is Most Accurate | Vima Fitness", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.vimafitness.com/blog/photo-calorie-counter-apps/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-18", "status": "rejected", "reason": "Developer claim (group B, not this cluster). Its evidence is Nutrition5k (CVPR 2021), which is a paper by the founder's own former team about research models, not a test of the shipping app — already recorded here as SCI-03. No independent validation of the SnapCalorie app was found in the peer-reviewed literature.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie FAQ — '~15% mean caloric error'", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.snapcalorie.com/faq.html", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-19", "status": "rejected", "reason": "CHECKED, ABSENT: no validation of its energy output exists. Confirmed by the MyFoodRepo paper (SCI-21), verbatim: 'One exception is Snap-n-Eat, a mobile phone recognition system able to perform automatic segmentation and classification of foods and allowing subsequent weight estimation and energy and macronutrient content calculation, but this app has not yet been validated'.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Snap-n-Eat: Food Recognition and Nutrition Estimation on a Smartphone", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.researchgate.net/publication/275361964_Snap-n-Eat_Food_Recognition_and_Nutrition_Estimation_on_a_Smartphone", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-sci-20", "status": "rejected", "reason": "Not opened in full this pass; duplicate product and paradigm with SCI-22 (Keenoa, image-ASSISTED with dietitian correction), and its comparator (ASA24 web recall) is another self-report instrument rather than weighed food. Flagged for a later pass if a Keenoa-specific record is wanted.", "group": "A-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Relative Validation of an AI-Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study (Keenoa vs ASA24)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9723975/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-21", "status": "rejected", "reason": "Live URL returns 404 today; archived captures (2023-09-27, 2024-02-04, 2024-04-16, 2024-08-07, 2025-02-17, 2025-11-08) are client-rendered SPA shells whose archived HTML contains no text, so no claim could be read from them. Sister host vision.foodvisor.io (docs, captures 2024-07-07 .. 2025-03-21) no longer resolves in DNS.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Foodvisor Vision (API landing page)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.foodvisor.io/en/vision/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-22", "status": "rejected", "reason": "Snapshots from 2021 onward archive only the JS shell (67 chars of text); content unrecoverable. The 2018 and 2020 snapshots did render and are used in the records.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Foodvisor homepage snapshots 2020-2026", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://web.archive.org/web/20220118082231/https://www.foodvisor.io/en/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-23", "status": "rejected", "reason": "Opened; vendor-adjacent post about the training hardware, contains no accuracy percentage (only 'get the accuracy high enough in six months' from Lose It's data scientist). No number to register.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lose It!'s New Food Recognition App Counts Calories (NVIDIA developer blog, 2016-10-04)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://developer.nvidia.com/blog/lose-its-new-food-recognition-app-counts-calories/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-24", "status": "rejected", "reason": "Opened; developer-written award entry, but its only percentage (72.7% of active users achieved clinically significant weight loss) is a weight-loss outcome citing an NIH study, not a recognition or calorie accuracy figure.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Snap It by Lose It! — Shorty Awards entry (2016/2017)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://shortyawards.com/9th/snap-it-by-lose-it", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-25", "status": "rejected", "reason": "Opened; page yielded no accuracy percentage for Meal Scan. A '97%' attributed to Passio circulates via mindinventory.com (an agency portfolio page), not via Passio itself — not traceable to a supplier statement, so not registered.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "MyFitnessPal — Nutrition AI by Passio (supplier case study)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.passio.ai/case-studies/myfitnesspal", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-26", "status": "rejected", "reason": "Not developer-origin (out of this cluster's scope) and the site presents suspiciously uniform 'benchmark' figures across many apps with no published protocol. Passed to leads for the editorial/benchmark cluster rather than registered here.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "MyFitnessPal AI Review 2026: Meal Scan Accuracy 71.2% | Benchmark", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://ai-food-tracker.com/reviews/myfitnesspal/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-27", "status": "rejected", "reason": "Surfaced repeatedly in search snippets with a very specific Foodvisor figure (MAPE ±5.3% on 231 meals) that appears in no primary source I could open; not developer-origin. Referred to leads for verification by the group-A cluster.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "foodvision-bench — 'Open reproducible benchmarks for food-image recognition models and APIs'", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://github.com/foodvision-bench/foodvision-bench", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-28", "status": "rejected", "reason": "Competitor-marketing content: asserts YAZIO 'has no true AI photo tracking, only barcode scanning', which is contradicted by YAZIO's own help centre and factsheet. Unreliable; not developer-origin.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Kann Yazio Lebensmittel von Fotos scannen? (2026 Realitätstest) / Nutrola blog series", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://nutrola.app/en/blog/can-yazio-scan-food-from-photos", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-29", "status": "rejected", "reason": "Competitor/affiliate comparison page quoting an unsourced '80-90% precision' for Foodvisor; not developer-origin and no method.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Foodvisor vs Yazio (Atlo)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.atlo.health/fr/blog/foodvisor-vs-yazio", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app1-30", "status": "rejected", "reason": "Not rejected as a source (it is registered), but noted: the host returns HTTP 403 to WebFetch and to plain curl; content had to be obtained through a rendering proxy and cross-checked against the 2021 Wayback capture.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "MyFitnessPal Meal Scan blog — direct fetch", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://blog.myfitnesspal.com/meal-scan/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-31", "status": "rejected", "reason": "Dead link on 2026-09-05: TLS certificate expired, WebFetch refuses. Wayback CDX returned no usable index for this path during the session (archive.org CDX intermittently offline). Could not confirm any numeric claim.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Bite AI - Food Recognition API", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://bite.ai/food-recognition/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-32", "status": "rejected", "reason": "Live page unreachable on 2026-09-05 (expired TLS certificate). Used the 2017-05-17 Wayback snapshot instead (recorded as app2-11).", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Press - Bitesnap (live)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://getbitesnap.com/press/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-33", "status": "rejected", "reason": "HTTP 403 Forbidden on fetch. Snippet-only; per brief, snippets are not sufficient. Left as a lead — it is a probable reprint node for the 90% claim and a popularity source.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Cal AI: How a teenage CEO built a fast-growing calorie-tracking app (CNBC, 2025-09-06)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.cnbc.com/2025/09/06/cal-ai-how-a-teenage-ceo-built-a-fast-growing-calorie-tracking-app.html", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-34", "status": "rejected", "reason": "HTTP 429 rate-limited; could not open the primary page to verify the store-page text or ratings count. Not recorded.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Cal AI - Calorie Tracker, Apple App Store listing", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://apps.apple.com/us/app/cal-ai-calorie-tracker/id6480417616", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-35", "status": "rejected", "reason": "Snapshot renders empty (JavaScript-built Ghost blog); no text extractable. Homepage snapshots of snapcalorie.com from 2021-2024 are likewise empty JS shells, so the site could not be used to date the 15% claim earlier than the 2025-01-21 FAQ capture.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie blog index (2023-06-05 snapshot)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.snapcalorie.com/blog/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-36", "status": "rejected", "reason": "Competitor-product SEO/affiliate content repeating SnapCalorie's own 15/20/40/53 numbers with no independent measurement. Duplicate reprint of app2-04.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "AI Calorie Estimation: Accuracy Explained", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://whatthefood.io/blog/ai-calorie-estimation-accuracy-explained", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-37", "status": "rejected", "reason": "Domain styled to look like a clinical journal but is a product-comparison page; brief warns that an authoritative-looking name is not sufficient. Not opened as a source of developer claims; flagged to the editorial-test agent instead.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie vs PlateLens vs Cronometer: Accuracy Under Three Protocols (2026)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://clinicalnutritionreport.com/articles/snapcalorie-vs-platelens-vs-cronometer-accuracy-protocols-2026/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-38", "status": "rejected", "reason": "Affiliate/marketing review restating vendor numbers. Duplicate reprint; also outside this cluster's scope (editorial tests).", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie Review: Accuracy, Features & Worth It?", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://macaron.im/blog/snapcalorie-review-2026", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-39", "status": "rejected", "reason": "Competitor-authored comparison, restates vendor numbers. Duplicate reprint.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "NutriScan vs SnapCalorie", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://nutriscan.app/blog/posts/nutriscan-vs-snapcalorie-ai-food-tracker-e2e26890e9", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-40", "status": "rejected", "reason": "Competitor-authored comparison page; no original measurement. Duplicate reprint.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Cal AI vs SnapCalorie", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://nutrola.app/en/blog/cal-ai-vs-snap-calorie-which-is-better-2026", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-41", "status": "rejected", "reason": "Unrelated-vendor content-marketing blog; restates the 90%/80% figures. Duplicate reprint, and out of scope (belongs to the editorial-test agent).", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Cal AI review: Does the calorie tracker actually work?", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.eesel.ai/blog/cal-ai", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-42", "status": "rejected", "reason": "Competitor-authored (Kalo) blog. Contains the useful observation that the 90% claim has zero citations, but it is not a developer-origin source. Moved to leads.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Is Cal AI Accurate? What the Evidence Actually Shows", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.getkalohealth.com/blog/is-cal-ai-accurate", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-43", "status": "rejected", "reason": "Auto-generated directory listing; no dated original claim.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "SnapCalorie product listing (MOGE)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://moge.ai/product/snapcalorie", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-44", "status": "rejected", "reason": "Opened and read: contains NO accuracy number and no user count, so it cannot corroborate the 87.3/97.1 figures. Kept only as context (CEO Charles Teague on why the flow is semi-automated).", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lose It app promises to log your meal just by taking its photo", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.engadget.com/2016-09-29-lose-it-snap-it-app.html", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-app2-45", "status": "rejected", "reason": "Surfaced in search but not opened; no numeric accuracy claim appeared in any Azumio-controlled page that was opened, and the caloriemama.ai snapshot series (2017-2024) already establishes the negative finding.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Calorie Mama Features (Azumio)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.azumio.com/apps/calorie-mama/features", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-46", "status": "rejected", "reason": "FABRICATED AUTHORITY — do not use. Presents itself as a peer-style paper from a 'Karolinska-anchored academic consortium'. Red flags, all checked: (a) the stated DOI '10.5281/zenodo.dai-2026-01' is not a valid Zenodo DOI (Zenodo DOIs are 10.5281/zenodo.); (b) it reports PlateLens photo mode at 1.1% MAPE on 180 mixed meals — an accuracy no weighed-food study has ever approached and below the tolerance of the nutrient databases used as ground truth; (c) the numbers do not even agree with the syndicated write-up of the same 'study' on consumertechwire.com (MacroFactor 4.8 vs 4.7, Cronometer 6.8 vs 5.9, and Cal AI 11.6% appears in the write-up but is absent from the paper); (d) all cited background references (Smith 2024, Ahmed & Lindqvist 2023, Nakamura 2022, Patel & Tanaka 2024, Weiss 2022, Moreno 2024, Tanaka & Brennan 2023) are non-locatable; (e) the winning product is a small commercial app whose own site advertises this paper. This is marketing dressed as a validation study.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Independent validation of six commercial AI-assisted dietary assessment applications against weighed-food reference: a 180-meal cross-sectional study (DAI-VAL-2026-01)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://dietaryassessmentinitiative.org/publications/six-app-validation-study-2026/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-47", "status": "rejected", "reason": "Part of the same PlateLens promotional network as dietaryassessmentinitiative.org. 'News' reprint of a fabricated study, with numbers that do not match the source it claims to report. Also publishes consumertechwire.com/analysis/two-independent-validations-platelens-2026/ presenting the fake study and the fake GitHub benchmark as 'two independent labs'. Not a check.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "DAI 2026 Study Benchmarks Six Calorie Apps; PlateLens Hits ±1.1% MAPE | Consumer Tech Wire", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://consumertechwire.com/news/dai-publishes-2026-six-app-validation-study/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-48", "status": "rejected", "reason": "Same network. Repo created 2026 under an anonymous single-user account, all commits by 'Foodvision Bench Contributors', monthly 'snapshots' in which PlateLens photo mode holds exactly 1.1% for seven consecutive snapshots while every competitor drifts. The 'weighed protocol' meals are said to be contributed from Amman and Beirut but no images, no raw data and no contributor identities exist. Reads as a manufactured reproducibility prop for the PlateLens 1.1% figure, not an open benchmark.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "foodvision-bench — 'Open reproducible benchmarks for food-image recognition models and APIs'", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://github.com/foodvision-bench/foodvision-bench", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-49", "status": "rejected", "reason": "Undisclosed vendor benchmark. The winner is 'Welling' (welling.ai) with ±0.7% portion error, 97.4% top-1 ID 'across 22,400 reference meals' and a 487-user 21-country 120-day adherence panel — a research programme no consumer review site could run, and inconsistent with the 50 weighed meals the methodology section actually describes. Numbers exist to rank one product first. Not an independent check.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Calorie Tracker Accuracy Test: 9 Apps Compared Across Real Meals", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.calorie-counters.com/calorie-tracker-accuracy-test/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-50", "status": "rejected", "reason": "Vendor testing its own product against competitors; Nutrola places first on every day. Also a category mismatch for this cluster: eight of the nine apps were exercised in MANUAL database-entry mode, so the reported deviations are database-lookup errors, not photo-estimation errors. Reference is USDA/McCance database values for self-weighed ingredients, i.e. partly the same databases the apps query. Not independent.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Same Meals, 9 Apps: Calorie Tracking Comparison Experiment 2026 | Nutrola", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://nutrola.app/en/blog/we-logged-the-same-meals-in-10-apps-heres-what-happened", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-51", "status": "rejected", "reason": "Vendor blog for the Open Health app. Real hands-on comparison of four photos across five apps, but NO reference measurement of any kind: results are star ratings and check/warning marks. No calorie numbers, no ground truth. Nothing to verify or chart.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "AI Photo Calorie Tracking: 5 Apps Tested for Accuracy (2026) — Open Health", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://openhealth.blog/en/blog/ai-photo-calorie-tracking", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-52", "status": "rejected", "reason": "Vendor (Peony) publishing 'registered dietitians tested 7 apps with 100+ real meals over 3 months' with its own app in the top two. No protocol, no reference method, no per-app numbers, no dietitian named. Unverifiable vendor claim, widely recycled by AI search summaries.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Best AI Calorie Counter Apps 2025: Expert Testing Results | Peony", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.heypeony.com/blog/best-a-i-calorie-counter", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-53", "status": "rejected", "reason": "No original measurement. Attributes its accuracy ranges to unnamed 'independent tests from fitness YouTubers and nutrition coaches' with no links, dates or numbers. Generic SEO review.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Cal AI Review 2026: Is Photo Calorie Counting Accurate? | Aumiqx", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://aumiqx.com/ai-tools/cal-ai-app-review-nutrition-tracker-2026/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-54", "status": "rejected", "reason": "News reprint, not a check. Careful and correctly hedged German write-up of the NIDDK/NIH NUTRITION 2026 conference abstract (102 metabolic-kitchen meals, MyFitnessPal / Lose It! / CalAI / Appediet, −250 to −345 kcal per meal). Performs no measurement of its own; the page also discloses AI-assisted research/editing. Belongs to the academic-source cluster.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Ein Foto, eine Kalorienzahl: Was Apps übersehen – und wie sich die Schätzung prüfen lässt | TechZeitGeist", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.techzeitgeist.de/foto-kalorien-apps-schaetzung-pruefen/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-55", "status": "rejected", "reason": "Duplicate reprint of the UVA Today / Sibylle Kranz piece (kept as record test-06). Adds no measurement. Same for techxplore.com, t3n.de 1761079, diningandcooking.com, newsbeep.com.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "AI Diet-Tracking Apps: Expert Test Reveals Surprising Risks | Progressive Robot", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.progressiverobot.com/2026/09/01/nutrition-expert-tested-ai-diet-tracking-apps/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-56", "status": "rejected", "reason": "Editorial commentary on a peer-reviewed study (52 standardised food photographs; ChatGPT-4o / Claude 3.5 Sonnet / Gemini 1.5 Pro; energy MAPE 35.8 / 35.8 / 64.2%). Biolayne measured nothing; the numbers belong to the academic cluster's primary paper.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Can We Use AI To Accurately Track Calories with A Picture? | Biolayne", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://biolayne.com/reps/issue-44/can-we-use-ai-to-accurately-track-calories-with-a-picture/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-57", "status": "rejected", "reason": "Genuinely in scope and genuinely regrettable: a one-day self-experiment in which the author tracked every meal on a food scale and compared the total to ChatGPT-4o under several prompting strategies. The numbers are behind Medium's member paywall. Direct fetch returns HTTP 403, the towardsai.net mirror and the Wayback snapshot (20260606231657) both stop at the paywall teaser, and freedium returns nothing. Numbers not obtainable — recorded here so the source is not lost.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "I Used ChatGPT to Count My Calories — Dr. Leon Eversberg, Towards AI (2025-02-04)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://pub.towardsai.net/i-used-chatgpt-to-count-my-calories-fabd14f0538b", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-58", "status": "rejected", "reason": "A real German editorial test of Foodvisor, behind the heise+ paywall. Wayback has 10+ snapshots (earliest 2025-01-17) and ALL stop at the same Leseprobe, so the numbers are not recoverable. The archived teaser does add verifiable metadata worth keeping: author Alexander Spier, 16.01.2025 13:24, 14 min read, heise+ exclusive, table of contents 'Foodvisor will beim Abnehmen helfen / Bildanalyse durch die KI / Fazit', and — new — the test ALSO put the same meals to ChatGPT as a comparator ('Zudem fragen wir ChatGPT zu seiner Meinung zum jeweiligen Essen... und wie korrekt die KI die Kalorien bestimmt'). No accuracy figure, no reference method, no app version readable. Still not verifiable.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Kalorienzählen mit KI: Foodvisor im Test | heise online (Alexander Spier, 2025-01-16)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.heise.de/tests/Kalorienzaehlen-mit-KI-Foodvisor-im-Test-10195446.html", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-59", "status": "rejected", "reason": "SUPERSEDED — no longer a rejection. The 2026-03-12 Wayback snapshot (web.archive.org/web/20260312140725/...) captured the article past the paywall, so the full 31-day Yazio/Garmin/SnapCalorie self-experiment is readable. Promoted to record test-11 (chart_excluded: the piece contains no calorie reference, only misidentification examples, one unreferenced 513 kcal app output, and a -2.9% body-weight outcome).", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Kalorienzähl-Apps: Ich fotografiere mein Essen, damit Sie es nicht tun müssen | Handelsblatt (2026-03-12)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.handelsblatt.com/unternehmen/handel-konsumgueter/kalorienzaehl-apps-ich-fotografiere-mein-essen-damit-sie-es-nicht-tun-muessen-02/100204647.html", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-60", "status": "rejected", "reason": "Launch-period French coverage, not a test. Mentions that a chocolate cake photographed with Cal AI gave values 'assez éloignées d'un calcul classique', but gives no numbers and does not say who performed that comparison. The only figure on the page ('précision à 90% selon les développeurs') is a developer claim and belongs to the developer-claim cluster.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Cal AI vous donne les calories de votre assiette en la prenant en photo | Mac4Ever (2025-03-18)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.mac4ever.com/iphone/187960-cal-ai-vous-donne-les-calories-de-votre-assiette-en-la-prenant-en-photo", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-61", "status": "rejected", "reason": "Round-up built from vendor claims and German app-store reviews; the headline 'präziser' is not backed by any measurement. The one numeric accuracy figure on the page (±0.20% body-fat error) is a manufacturer claim about a body scan, not calorie estimation.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "MyFitnessPal-Killer: Diese neuen Kalorienzähl-Apps sind präziser | Men's Health DE", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.menshealth.de/abnehmen/myfitnesspal-killer-diese-neuen-kalorienzaehl-apps-sind-praeziser/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-62", "status": "rejected", "reason": "Product-announcement write-up of the Qalzy AI kitchen scale. No hands-on test, no calorie numbers, no reference.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "This kitchen scale uses AI to weigh your food and track calories — here's how it works | Tom's Guide", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.tomsguide.com/home/kitchen-dining/this-kitchen-scale-uses-ai-to-weigh-your-food-and-track-calories-heres-how-it-works", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-63", "status": "rejected", "reason": "Page not retrievable — Cloudflare block on both curl and WebFetch. Could not confirm whether any measurement was performed, so not usable under the 'open the primary page' rule.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Honest Review of SnapCalorie AI-Powered Tracker | WellnessPulse", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://wellnesspulse.com/nutrition/snapcalorie-ai-image-tracker-review/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-64", "status": "rejected", "reason": "Not retrievable (JS/anti-bot wall on curl; HTTP 403 on WebFetch). Content could not be verified.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Die besten KI-Ernährungstracker für iOS und Android | futurezone.at", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://futurezone.at/apps/beste-ki-ernaehrung-tracker-android-ios-mahlzeit-kalorien-abnehmen-muskeln/403098613", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-65", "status": "rejected", "reason": "Described in search results as a mini-benchmark across GPT-4o, o3 and Claude, which would be in scope. Medium blocks retrieval (Cloudflare), no archive snapshot, numbers unverifiable. Snippet-only evidence is not enough.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Do you use LLMs to help you track your calories? Here's how to make them more accurate | Tech Talk with Tyler, Medium", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://medium.com/@ceo_44783/do-you-use-llms-to-help-you-track-your-calories-heres-how-to-make-them-more-accurate-e517ddacea01", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-66", "status": "rejected", "reason": "Representative of a whole genre (also youtube.com/watch?v=V_R8zUnYNPY, YgYeRZqiY5Y, iZcBCoy7upY, r6SCAaPAxm8) of creator videos that do weigh food and compare it to app output. None could be verified within this cluster's tooling: transcripts are not fetchable, per-dish numbers appear only on screen, and reference method, dates and app versions are not stated in any fetchable text. Left unverified rather than summarised second-hand.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "What AI Photo Calorie Tracker Is Most Accurate? (CalAI vs. Snap Calorie vs. MORE) — YouTube (2025-09-28)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.youtube.com/watch?v=ZoirXvRf9yc", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-67", "status": "rejected", "reason": "Grouped rejection. A cluster of sites presenting as consumer-advice, clinical, dietitian and benchmark authorities, all converging on the same product and the same ±1.1% figure, and cross-citing one another as 'independent'. Treated as one marketing voice, not as multiple checks. Recorded because these pages dominate search results for 'AI calorie app accuracy' and will otherwise be mistaken for evidence.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "PlateLens promotional network (grouped rejection)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://calorierankings.com/blog/can-ai-accurately-count-calories-from-photos/ , https://clinicalnutritionreport.com/rankings/best-ai-photo-calorie-tracker-2026/ , https://thereviewbench.com/reviews/platelens-review/ , https://rdrecommended.com/articles/best-calorie-counting-app-registered-dietitian-picks-2026/ , https://glp1nutrition.org/apps/platelens-glp1-review/ , https://www.bitebench.com/articles/every-calorie-tracking-app-compared-2026-11-apps-tested/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-68", "status": "rejected", "reason": "Grouped rejection. Each is published by a calorie-app vendor and recycles the same third-party figures (NIH −250/−345 kcal; 68–86% identification; 39% portion reliability; 35.8/64.2% LLM MAPE) with no measurement of its own, usually with round unsourced additions ('85–95% on single foods, 25–35% on mixed meals'). Ten sites repeating one number is not ten checks. They are the reason the search space for this cluster is almost unusable.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Competing-app SEO blogs restating academic numbers (grouped rejection)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.getkalohealth.com/blog/is-cal-ai-accurate , https://kcalm.app/blog/chatgpt-calorie-counting-accuracy/ , https://nutrola.app/en/blog/how-accurate-is-chatgpt-for-calorie-estimates , https://www.intakenutrition.io/blog/is-cal-ai-accurate-what-public-reviews-and-ai-research-actually-suggest , https://whatthefood.io/blog/how-accurate-are-ai-calorie-counters , https://www.amyfoodjournal.com/blog/ai-calorie-counter-apps , https://fitia.app/learn/article/ai-calorie-photo-apps-accuracy-2026/ , https://macaron.im/blog/snapcalorie-review-2026 , https://calzy-app.com/blog/best-ai-calorie-counter-apps , https://nutriscan.app/blog/posts/best-free-ai-calorie-tracking-apps-2025-bd41261e7d , https://www.vimafitness.com/blog/photo-calorie-counter-apps/ , https://caleye.fit/blog/ai-vs-dietitian-accuracy-studies/ , https://aicaloriecalculator.org/can-ai-estimate-calories-from-a-picture/ , https://www.eesel.ai/blog/cal-ai", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-69", "status": "rejected", "reason": "PlateLens promotional network — same cluster already rejected above. The page presents a house 'benchmark' with no protocol, no meal count, no reference method and no data, and every comparison resolves to PlateLens ('94.3% vs 71.2% identification, ±1.2% vs ±18% portion MAPE, 2.8s vs 8.4s'). The ±18% figure has no published methodology anywhere on the site. Not a real benchmark; do not treat 71.2%/±18% as measured values.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "MyFitnessPal review — 'AI Food Tracker' (claims 71.2% food identification, ±18% portion MAPE 'in our benchmark')", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://ai-food-tracker.com/reviews/myfitnesspal/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-70", "status": "rejected", "reason": "Same network. Attributes '±18% MAPE on weighed reference meals' to 'DAI's 2026 May validation' — i.e. to the fabricated Dietary Assessment Initiative study already rejected above — and adds '±22% in our testing' with no protocol. Steers to PlateLens at ±1.2% in six separate places. Notably it also states 'We don't accept affiliate compensation, app sponsorships, or paid placements', which is an independence claim sitting on top of a fabricated citation; treat the disclaimer as part of the marketing, not as evidence of independence.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Lifesum review — bestcalorieapps.com", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://bestcalorieapps.com/en/reviews/lifesum/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-71", "status": "rejected", "reason": "Opinion/product-critique essay, not a test. Argues that Yazio's photo AI 'liefert oft nur eine Annaeherung' and shifts work rather than removing it, but performs no measurement: no dish, no photo, no reference, no number anywhere in the piece.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "YAZIO als KI-Kalorienzähler: viel Hype, aber hilft das den Nutzern wirklich? | check-app.de (2026-04-15)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.check-app.de/2026/04/15/yazio-als-ki-kalorienzaehler-viel-hype-aber-hilft-das-den-nutzern-wirklich/", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-72", "status": "rejected", "reason": "Hands-on app review, but the photo feature is covered in one clause ('there's a photography snap-n-log feature as well, but it needs some work') with no dish, no reference and no calorie numbers. Site-wide affiliate disclosure and monetised app links. Nothing measurable for this cluster.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Foodvisor Review | Garage Gym Reviews", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.garagegymreviews.com/foodvisor-review", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-73", "status": "rejected", "reason": "Not retrievable — the host returns Cloudflare error 525 (SSL handshake failed) to both curl and the reader proxy. Could not confirm whether any measurement exists or who publishes the site, so it cannot be used under the 'open the primary page' rule. Note that trygaya.com is itself a nutrition-app vendor, so any accuracy ranking it publishes would need the same vendor scrutiny applied above.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Gaya app-review pages (Yazio, Lose It)", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "https://www.trygaya.com/review/yazio-review , https://www.trygaya.com/review/lose-it-review", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" }, { "record_id": "rej-test-74", "status": "rejected", "reason": "Searched repeatedly for Wired, The Verge, Wirecutter, Lifehacker, Digital Trends, Mashable and PCMag hands-on accuracy tests of the 2016-2023 generation of photo-calorie apps. None found. What exists from that era is (a) launch coverage that only repeats the vendor's own figure — phonearena.com on Lose It! Snap It carries the company claim of '87.3% up to 97.1%' accuracy, which belongs to the developer-claim cluster, and techcrunch.com/2023/06/26 on SnapCalorie's funding round; and (b) the 2020 academic comparison (PMC7752530) of Calorie Mama / Bitesnap / Foodvisor, which is food IDENTIFICATION accuracy (top-1 63% / 48.9% / 46.2%), not calorie error, and belongs to the academic cluster. Conclusion for the registry: for this cluster, the independent-test record effectively begins in 2024. Several of the outlets that would have run such tests (Wired, The Verge, CNET, NYT, Business Insider, PCMag, Reddit) are also unreachable to this user agent, so this is a negative result for accessible sources, not proof of non-existence.", "group": "B-candidate", "subtype": "", "independent": "", "chart_title": "", "chart_metric": "", "chart_low_percent": "", "chart_high_percent": "", "chart_is_lower_bound": "", "chart_is_claim": "", "chart_note": "", "chart_limit": "", "title": "Pre-LLM-era editorial photo-calorie accuracy tests — NOT FOUND", "product": "", "product_version": "", "measured_by": "", "author_org": "", "publisher": "", "language": "", "pub_date": "", "test_date": "", "archive_date": "", "checked_date": "2026-09-05", "url": "(negative result) 2015-2023 English-language editorial tests of Calorie Mama, Bitesnap, Lose It! Snap It, Foodvisor", "archive_url": "", "sample_size": "", "unit": "", "reference_and_conditions": "", "original_metric": "", "original_numbers": "", "quote_or_location": "", "registry_metric": "", "registry_low_percent": "", "registry_high_percent": "", "range_type": "", "recomputation_formula": "", "assumptions": "", "result_status": "", "commercial_ties": "", "ties_note": "", "limitations": "", "reprint_chain": "", "number_origin": "", "coordinator_note": "", "second_pass_note": "" } ] }