ORIGINAL RESEARCH article
Front. Nutr.
Sec. Nutrition Methodology
This article is part of the Research TopicRevolutionizing Personalized Nutrition: AI's Role in Chronic Disease Management and Health Improvement-Volume IIView all articles
Artificial Intelligence Diet Plans Underestimate Nutrient Intake Compared to Dietitians in Adolescents
Provisionally accepted
- 1İstanbul Atlas University, Istanbul, Türkiye
- 2Istanbul Medeniyet Universitesi, Istanbul, Türkiye
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Objective: Although artificial intelligence (AI)-based nutrition recommendations are becoming increasingly common among the public, the accuracy and reliability of diets produced especially for adolescents in the growth and development period are not sufficiently known. This study aimed to evaluate the clinical validity of AI by comparing the nutritional content of diets generated by different AI models with dietitian reference plans. Methods: A total of 60 three-day diet plans were generated in two sessions by five AI models (ChatGPT-4o, Gemini 2.5 Pro, Claude 4.1, Bing Chat-5GPT, and Perplexity) for four standardized adolescent profiles in this cross-sectional and comparative study. A dietitian reference plan was prepared for each profil. Energy and macro-micronutrients were analyzed with BeBiS. Comparisons were evaluated with single-sample t-test, Cohen's d, and Bland–Altman fit analyses. Results: AI models tended to systematically undercalculate energy (bias: +695 kcal), protein (+19.9 g), lipid (+15.8 g), and carbohydrate (+114.6 g). In macronutrient percentages, protein (21.5–23.7%) and lipid (41.5–44.5%) ratios were above the recommended adolescent guidelines, while carbohydrate ratios (32.4–36.3%) were significantly below. Significant variation was observed between models in micronutrient contents, and no model showed consistent proximity to the dietitian across all nutrients. Conclusion: AI models have exhibited clinically significant deviations in diet plans for adolescents at both macro and micro levels. The findings indicate that AI-based dietary recommendations are not appropriate to use without professional supervision, emphasizing the need for model improvements for more reliable data generation in this area.
Keywords: Adolescent nutrition3, Artificial Intelligence1, Diet planning4, Large Language Models2, Nutrient adequacy5
Received: 11 Dec 2025; Accepted: 20 Jan 2026.
Copyright: © 2026 BİLEN, KALKAN and YILMAZ ÖNAL. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ayşe Betül BİLEN
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