Med-Diet LLM agent outperforms general purpose models in generating dietary plans for noncommunicable diseasesSpecialized AI Tool Shows Better Results for Complex Dietary Plans

BackgroundScientifically grounded and clinically applicable dietary management is essential for patients with chronic diseases. However, in routine practice, nutritionists frequently lack efficient and scalable tools to deliver targeted, guideline-consistent nutritional guidance across diverse and complex clinical scenarios.ObjectiveTo develop and conduct an exploratory expert-rating evaluation of Med-Diet, a large language model (LLM)—based agent for generating dietary plans for chronic diseases.MethodsWe built Med-Diet using DeepSeek-R1, integrated clinical dietary guidelines, evaluated it on 79 real cases covering common, rare, and complex noncommunicable diseases, and compared it with four general-purpose LLMs (DeepSeek-R1, GPT-4o, GLM-Z1-32B, and Llama-3.3-70B). Fourteen clinical experts from different fields conducted blinded, multidimensional ratings of generated dietary plans. Furthermore, an exploratory comparative experiment assessed nutritionists’ efficiency and output quality without and with Med-Diet assistance.ResultsMed-Diet received higher mean preference scores from expert evaluators compared to all baseline LLMs (mean score of 4.09 ± 0.64). Expert ratings suggested superior performance for Med-Diet in dimensions including accuracy, safety, nutritional balance, personalization, practicality, and overall recommendation (all p