An Analysis of the Nutritional Value of Diets Generated by Large Language Models Under Average User Conditions-3(LM)Diet Study.

Background/

Objectives

AI tools are becoming increasingly common, including in areas related to health and diet. However, there are no studies indicating the nutritional value of diets generated by large language models (LLMs). The aim of this study was to analyse the nutritional value of diets generated by three major language models.

Methods

Three LLMs were used in the study: ChatGPT, Gemini and Copilot. Using each of these, 35-day meal plans were generated for three energy variants: 2000 kcal, 2500 kcal and 3000 kcal. The meal plans were then analysed using dietary software.

Results

All LLMs generated meal plans with an insufficient energy value (p < 0.001, one-sample t-test) by 284-546 kcal, depending on the model and the targeted energy content. The meal plans were characterised by an excessively high proportion of energy from protein and an excessively low proportion of energy from fat. Carbohydrates were planned in the correct amounts. ChatGPT and Copilot generated meal plans with a generally adequate vitamin and mineral content. Gemini generated meal plans that were often deficient, particularly for diets with lower energy values.

Conclusions

Diets generated by LLMs have many shortcomings. Unsupervised use of LLM-generated meal plans by non-expert users may produce nutritionally inaccurate diets and should not replace professional dietary counselling.