Scholarlink

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

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring Large Language Model Reasoning Capabilities Over Personal Health Data

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Gianluca Apriceno,
  • Tania Bailoni,
  • Mauro Dragoni

摘要

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