An Evaluation of ChatGPT for Nutrient Content Estimation from Meal Photographs Article Swipe
Cathal O’Hara
,
Gráinne Kent
,
Angela C. Flynn
,
Eileen R. Gibney
,
Claire Timon
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/nu17040607
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/nu17040607
Background/Objectives: Advances in artificial intelligence now allow combined use of large language and vision models; however, there has been limited evaluation of their potential in dietary assessment. This study aimed to evaluate the accuracy of ChatGPT-4 in estimating nutritional content of commonly consumed meals using meal photographs derived from national dietary survey data. Methods: Meal photographs (n = 114) were uploaded to ChatGPT and it was asked to identify the foods in each meal, estimate their weight, and estimate the nutrient content of the meals for 16 nutrients for comparison with the known values using precision, paired t-tests, Wilcoxon signed rank test, percentage difference, and Spearman correlation (rs). Seven dietitians also estimated energy, protein, and carbohydrate content of thirty-eight meal photographs for comparison with ChatGPT using intraclass correlation (ICC). Results: Comparing ChatGPT and actual meals, ChatGPT showed good precision (93.0%) for correctly identifying the foods in the photographs. There was good agreement for meal weight (p = 0.221) for small meals, but poor agreement for medium (p < 0.001) and large (p < 0.001) meals. There was poor agreement for 10 of the 16 nutrients (p < 0.05). Percentage difference from actual values was >10% for 13 nutrients, with ChatGPT underestimating 11 nutrients. Correlations were adequate or good for all nutrients with rs ranging from 0.29 to 0.83. When comparing ChatGPT and dietitians, the ICC ranged from 0.31 to 0.67 across nutrients. Conclusions: ChatGPT performed well for identifying foods, estimating weights of small portion sizes, and ranking meals according to nutrient content, but performed poorly for estimating weights of medium and large portion sizes and providing accurate estimates of nutrient content.
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Metadata
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- OpenAlex ID
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https://openalex.org/W4407252894Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/nu17040607Digital Object Identifier
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An Evaluation of ChatGPT for Nutrient Content Estimation from Meal PhotographsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-02-07Full publication date if available
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Cathal O’Hara, Gráinne Kent, Angela C. Flynn, Eileen R. Gibney, Claire TimonList of authors in order
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https://doi.org/10.3390/nu17040607Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/nu17040607Direct OA link when available
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Meal, Food science, Nutrient, Estimation, Biology, Ecology, Economics, ManagementTop concepts (fields/topics) attached by OpenAlex
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2025: 13Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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