A Decade-Scale Benchmark Evaluating LLMs' Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations

📰 ArXiv cs.AI

Researchers introduce CPGBench, a benchmark to evaluate LLMs' ability to detect and adhere to clinical practice guidelines in multi-turn conversations

advanced Published 27 Mar 2026

Action Steps

Develop a dataset of multi-turn conversations related to healthcare scenarios
Implement CPGBench, an automated framework to benchmark LLMs' clinical guideline detection and adherence capabilities
Evaluate LLMs using CPGBench to identify areas of improvement
Fine-tune LLMs to enhance their ability to detect and adhere to clinical practice guidelines

Who Needs to Know This

This research benefits AI engineers, ML researchers, and healthcare professionals working on LLMs for healthcare applications, as it provides a framework to assess and improve the models' ability to follow clinical guidelines

Key Insight

💡 CPGBench provides a framework to assess and improve LLMs' ability to follow clinical guidelines, ensuring evidence-based decision-making in healthcare

Key Takeaways

Researchers introduce CPGBench, a benchmark to evaluate LLMs' ability to detect and adhere to clinical practice guidelines in multi-turn conversations

Full Article

Title: A Decade-Scale Benchmark Evaluating LLMs' Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations

Abstract:
arXiv:2603.25196v1 Announce Type: cross Abstract: Clinical practice guidelines (CPGs) play a pivotal role in ensuring evidence-based decision-making and improving patient outcomes. While Large Language Models (LLMs) are increasingly deployed in healthcare scenarios, it is unclear to which extend LLMs could identify and adhere to CPGs during conversations. To address this gap, we introduce CPGBench, an automated framework benchmarking the clinical guideline detection and adherence capabilities of

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