# Large Language Models as Clinical Nutrition Decision Tools: Quantitative Bias and Guideline Deviation in Type 2 Diabetes Meal Planning

## TL;DR

AI-generated meal plans for type 2 diabetes differ from expert diets, showing bias in energy and fiber, and need expert evaluation before use.

## Contribution

This study quantitatively evaluates the bias and guideline adherence of large language models in generating dietary plans for type 2 diabetes.

## Key findings

- LLM-generated diets showed systematic deviations from dietitian-designed reference diets in energy and macronutrient content.
- AI meal plans lacked sufficient fiber and showed limited individualized medical nutrition therapy.
- Only one of the tested models showed relatively consistent guideline adherence.

## Abstract

What are the main findings?
Large language models generated dietary plans for type 2 diabetes that differed substantially from a guideline-based, dietitian-designed reference diet, particularly in energy intake and dietary fiber adequacy.Most AI-generated diets followed a low-energy, lower-carbohydrate, higher-protein, and insufficient-fiber pattern, with limited evidence of individualized medical nutrition therapy.

Large language models generated dietary plans for type 2 diabetes that differed substantially from a guideline-based, dietitian-designed reference diet, particularly in energy intake and dietary fiber adequacy.

Most AI-generated diets followed a low-energy, lower-carbohydrate, higher-protein, and insufficient-fiber pattern, with limited evidence of individualized medical nutrition therapy.

What are the implications of the main findings?
AI-generated dietary plans for diabetes management should not replace professional medical nutrition therapy without expert evaluation.Careful clinical validation and guideline-based refinement are required before integrating large language models into routine diabetes care.

AI-generated dietary plans for diabetes management should not replace professional medical nutrition therapy without expert evaluation.

Careful clinical validation and guideline-based refinement are required before integrating large language models into routine diabetes care.

Background/Objectives: Large language models (LLMs) are increasingly used as decision support tools in clinical nutrition, including meal planning for individuals with type 2 diabetes mellitus (T2DM). However, the clinical safety, quantitative accuracy, and guideline adherence of AI-generated dietary plans remain uncertain. This study aimed to evaluate systematic bias and agreement between LLM-generated diets and a guideline-concordant reference diet, and to assess whether current LLMs can function as reliable clinical nutrition decision support tools in T2DM. Methods: Six widely used LLMs generated standardized three-day, 1800 kcal dietary plans for T2DM using an identical prompt. Each day was treated as an independent observation (n = 18). Energy and macronutrient contents were analyzed using professional nutrition software and compared with a dietitian-designed reference diet based on ADA, EASD, IDF, and national guidelines. Agreement was evaluated using Bland–Altman analysis, proportional bias assessment, and intraclass correlation coefficients. Guideline adherence and clinical appropriateness were independently scored by registered dietitians. Results: Most LLM-generated diets systematically deviated from the reference diet, with lower total energy, reduced carbohydrate and fiber content, and variable protein distribution. Bland–Altman analyses demonstrated significant bias and wide limits of agreement for key nutrients, indicating clinically meaningful discrepancies. Guideline adherence scores varied substantially across models, with only one model showing relatively consistent performance. Inter-rater reliability between dietitians was high (ICC = 0.806). Conclusions: Current LLMs exhibit systematic quantitative bias and inconsistent guideline adherence when used for T2DM meal planning. AI-generated dietary plans are not interchangeable with dietitian-guided medical nutrition therapy and may pose clinical risks if used without professional oversight. Careful validation, domain-specific fine-tuning, and integration within supervised clinical workflows are required before implementation in diabetes care.