An Evaluation of ChatGPT for Nutrient Content Estimation from Meal Photographs.

ABSTRACT: 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 (r_s). Seven dietitians also e

SUBMITTER: O'Hara C 

PROVIDER: S-EPMC11858203 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature