Observational studyNutrients2026
Domain-Dependent Performance of Human Experts and AI Systems in Pediatric Menu Evaluation.
Observational study in Nutrients, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
7 authors.
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Abstract
BACKGROUND/
objectivesArtificial intelligence (AI) tools are increasingly used for dietary assessment, but their reliability in pediatric nutrition remains uncertain. This study compared AI systems and human evaluators in pediatric menu assessment against expert-defined reference ratings.
methodsThis observational cross-sectional study used anonymized dietary and clinical information from three healthy pediatric cases. A multidisciplinary panel of pediatric nutrition specialists established standard evaluations. Assessments were completed by 84 AI evaluations, 116 nutrition specialists, 56 pediatric-focused physicians, and 88 physicians from other specialties. Outcomes included absolute error in total energy estimation, deviations in portion and qualitative ratings, standardized scores, and binary adequacy accuracy. Groups were compared using ANOVA or Kruskal-Wallis tests with post hoc analyses.
resultsA total of 344 assessments were included. Energy-estimation error differed between groups (Kruskal-Wallis = 12.85,
conclusionsAI may support structured pediatric menu screening for descriptive qualitative features; however, lower precision for energy and portion assessment supports, under these specific conditions, its use as an adjunct, for qualified nutrition professionals.
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