Benchmarking Clinical Decision Pathway Adherence in Large Language Models
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Computer Science > Computation and Language
Title:Benchmarking Clinical Decision Pathway Adherence in Large Language Models
Abstract:Following clinical decision pathways (CDPs) defined by clinical practice guidelines is essential for safe and reliable medical decision-making. However, existing medical large language model (LLM) benchmarks mainly evaluate final-answer accuracy, providing limited evaluation of models' ability to adhere to guidelines. To address this gap, we introduce MEGA-CDP, a benchmark for evaluating whether medical LLMs can generate guideline-adherent CDPs using provided guidelines as references. MEGA-CDP is constructed from 2,274 English and Chinese clinical practice guidelines through a guideline-to-case pipeline, yielding 42,353 clinical cases with explicit reference CDPs. It supports both single-turn vignette and multi-turn interactive settings, and introduces a CDP-oriented evaluation framework for measuring pathway consistency. Experiments on 16 representative LLMs show that reliable clinical decision support remains challenging for current models, demonstrating the need for CDP-oriented evaluation and the value of MEGA-CDP for advancing guideline adherence in medical LLMs.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.26592 [cs.CL] |
| (or arXiv:2608.26592v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.26592
arXiv-issued DOI via DataCite (pending registration)
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