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This study explores the capabilities of Large Language Models in reasoning over structured clinical knowledge with a focus on temporally dependent monitoring rules. We assess the models’ performance across five real-world patient scenarios derived from a virtual coaching platform in the Trentino Salute 4.0 project. Using a structured prompt and diverse use cases, we examine whether these models can accurately detect and justify rule violations and generate clear, audience-specific explanations for patients and healthcare professionals. The results reveal that while Large Language Models demonstrate potential in interpreting complex, time-sensitive clinical data and adapting communication to different stakeholders, limitations persist in temporal reasoning, explanation consistency, and audience alignment. These findings offer insights into the strengths and challenges of deploying LLMs in personalized and temporally aware healthcare decision support systems. GitHub: https://github.com/IDA-FBK/LLM-Temporal-Clinical-Rea soning

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Assessing Temporal Reasoning of Large Language Models on Structured Temporal Clinical Data

  • 会议论文

  • 发表日期：

    2026年

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  • Gianluca Apriceno,
  • Tania Bailoni,
  • Mauro Dragoni

摘要

This study explores the capabilities of Large Language Models in reasoning over structured clinical knowledge with a focus on temporally dependent monitoring rules. We assess the models’ performance across five real-world patient scenarios derived from a virtual coaching platform in the Trentino Salute 4.0 project. Using a structured prompt and diverse use cases, we examine whether these models can accurately detect and justify rule violations and generate clear, audience-specific explanations for patients and healthcare professionals. The results reveal that while Large Language Models demonstrate potential in interpreting complex, time-sensitive clinical data and adapting communication to different stakeholders, limitations persist in temporal reasoning, explanation consistency, and audience alignment. These findings offer insights into the strengths and challenges of deploying LLMs in personalized and temporally aware healthcare decision support systems. GitHub: https://github.com/IDA-FBK/LLM-Temporal-Clinical-Rea soning