Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots

Kaya Kaçar, Hüsna; Kaçar, Ömer Furkan; Avery, Amanda

Abstract

Background/Objectives: With the rise of artificial intelligence (AI) in nutrition and healthcare, AI-driven chatbots are increasingly recognised as potential tools for generating personalised diet plans. This study aimed to evaluate the capabilities of three popular chatbots—Gemini, Microsoft Copilot, and ChatGPT 4.0—in designing weight-loss diet plans across varying caloric levels and genders.

Methods: This comparative study assessed the diet quality of meal plans generated by the chatbots across a calorie range of 1400–1800 kcal, using identical prompts tailored to male and female profiles. The Diet Quality Index-International (DQI-I) was used to evaluate the plans across dimensions of variety, adequacy, moderation, and balance. Caloric accuracy was analysed by calculating percentage deviations from requested targets and categorising discrepancies into defined ranges.

Results: All chatbots achieved high total DQI-I scores (DQI-I > 70), demonstrating satisfactory overall diet quality. However, balance sub-scores related to macronutrient and fatty acid distributions were consistently the lowest, showing a critical limitation in AI algorithms. ChatGPT 4.0 exhibited the highest precision in caloric adherence, while Gemini showed greater variability, with over 50% of its diet plans deviating from the target by more than 20%.

Conclusions: AI-driven chatbots show significant promise in generating nutritionally adequate and diverse weight-loss diet plans. Nevertheless, gaps in achieving optimal macronutrient and fatty acid distributions emphasise the need for algorithmic refinement. While these tools have the potential to revolutionise personalised nutrition by offering precise and inclusive dietary solutions, they should enhance rather than replace the expertise of dietetic professionals.

Citation

Kaya Kaçar, H., Kaçar, Ö. F., & Avery, A. (2025). Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots. Nutrients, 17(2), Article 206. https://doi.org/10.3390/nu17020206

• Journal Article Type: Article
• Acceptance Date: Jan 6, 2025
• Online Publication Date: Jan 7, 2025
• Publication Date: Jan 7, 2025
• Deposit Date: Feb 3, 2025
• Publicly Available Date: Feb 3, 2025
• Journal: Nutrients
• Electronic ISSN: 2072-6643
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 2
• Article Number: 206
• DOI: https://doi.org/10.3390/nu17020206
• Keywords: AI technology; caloric accuracy; chatbots; diet quality; personalised nutrition; weight-loss diets
• Public URL: https://nottingham-repository.worktribe.com/output/44824313
• Publisher URL: https://www.mdpi.com/2072-6643/17/2/206