AI Diet Plans: Evaluating Quality and Accuracy of Chatbot-Generated Diets

The integration of artificial intelligence (AI) into healthcare and nutrition is rapidly transforming how personalized diet plans are created and implemented. AI-driven chatbots, such as Gemini, Microsoft Copilot, and ChatGPT 4.0, are emerging as potential tools for generating customized weight-loss diets. This study aims to evaluate the capabilities of these chatbots in designing diet plans across varying caloric levels and genders, focusing on diet quality and caloric accuracy.

AI chatbots offer convenience and potential for personalized support, simulating human-like interactions through natural language processing and machine learning. These tools can provide tailored diet and exercise recommendations, motivational support, and encouragement to enhance adherence to weight management programs. However, questions remain regarding the accuracy and quality of the diet plans they produce, necessitating a systematic evaluation against established nutritional standards.

This comparative study assessed the diet quality of meal plans generated by Gemini, Microsoft Copilot, and ChatGPT 4.0 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 analyzed by calculating percentage deviations from requested targets.

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%.

The DQI-I assessment revealed that AI-generated diets generally excel in variety and adequacy, ensuring a diverse range of food groups and sufficient nutrient intake. However, the balance sub-score, which evaluates macronutrient and fatty acid ratios, consistently received the lowest scores across all chatbots. This indicates a significant gap in the ability of AI algorithms to optimize macronutrient balance, highlighting the need for algorithmic refinement.

ChatGPT 4.0 demonstrated the highest precision in meeting the requested caloric targets, with none of its diet plans deviating by more than 20%. In contrast, Gemini showed greater variability, with 50% of its diet plans exceeding the requested calorie target by more than 20%. This underscores the importance of validating and refining AI algorithms to ensure accurate caloric adherence.

The study also revealed gender-based differences in diet plan variety. The mean sub-scores for “variety—food groups” and “variety—protein sources” were significantly higher for diet plans designed for females compared to those for males. This suggests a potential bias or variability in tailoring diets to male versus female users, warranting further investigation and refinement of AI algorithms.

While AI-driven chatbots show significant promise in generating nutritionally adequate and diverse weight-loss diet plans, gaps in achieving optimal macronutrient and fatty acid distributions emphasize the need for algorithmic refinement. These tools have the potential to revolutionize personalized nutrition by offering precise and inclusive dietary solutions, but they should enhance rather than replace the expertise of dietetic professionals. Dietitians can leverage AI tools to streamline diet planning, while ensuring that the plans meet individual needs and adhere to established nutritional standards.

This study has some limitations, including the use of a specific calorie range and the focus on three popular chatbots. Future research should explore a wider range of calorie levels, incorporate more diverse AI tools, and consider individual preferences and cultural factors. Additionally, longitudinal studies are needed to assess the long-term efficacy and safety of AI-generated diet plans.

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 emphasize the need for algorithmic refinement. While these tools have the potential to revolutionize personalized nutrition by offering precise and inclusive dietary solutions, they should enhance rather than replace the expertise of dietetic professionals.