Exploring Large Language Model Reasoning Capabilities Over Personal Health Data

This study investigates Large Language Models’ capabilities in reasoning with structured clinical knowledge to identify and justify violations of health guidelines and to generate clear, audience-specific explanations for patients and physicians. Using a structured prompt and a use case involving dietary monitoring for a cardiovascular patient, we evaluate the performance of different Large Language Models. Preliminary findings highlight the potential of Large Language Models to effectively reason with structured health data and deliver tailored explanations, offering valuable insights into their role in personalized healthcare and decision support systems. GitHub: https://github.com/IDA-FBK/LLMReasoningPersonalHealthData