MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Models

• URL: http://arxiv.org/abs/2505.11613v1
• Date: Fri, 16 May 2025 18:21:52 GMT
• Title: MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Models
• Authors: Xiaomin Li, Mingye Gao, Yuexing Hao, Taoran Li, Guangya Wan, Zihan Wang, Yijun Wang,
• Abstract summary: We introduce MedGUIDE, a new benchmark for evaluating Large Language Models (LLMs) on their ability to make guideline-consistent clinical decisions.<n> MedGUIDE is constructed from 55 curated NCCN decision trees across 17 cancer types.<n>We apply a two-stage quality selection process, combining expert-labeled reward models and LLM-as-a-judge ensembles across ten clinical and linguistic criteria, to select 7,747 high-quality samples.
• Score: 10.46932473088646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical guidelines, typically structured as decision trees, are central to evidence-based medical practice and critical for ensuring safe and accurate diagnostic decision-making. However, it remains unclear whether Large Language Models (LLMs) can reliably follow such structured protocols. In this work, we introduce MedGUIDE, a new benchmark for evaluating LLMs on their ability to make guideline-consistent clinical decisions. MedGUIDE is constructed from 55 curated NCCN decision trees across 17 cancer types and uses clinical scenarios generated by LLMs to create a large pool of multiple-choice diagnostic questions. We apply a two-stage quality selection process, combining expert-labeled reward models and LLM-as-a-judge ensembles across ten clinical and linguistic criteria, to select 7,747 high-quality samples. We evaluate 25 LLMs spanning general-purpose, open-source, and medically specialized models, and find that even domain-specific LLMs often underperform on tasks requiring structured guideline adherence. We also test whether performance can be improved via in-context guideline inclusion or continued pretraining. Our findings underscore the importance of MedGUIDE in assessing whether LLMs can operate safely within the procedural frameworks expected in real-world clinical settings.

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