MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning
The article discusses MedGuideX, a new approach to integrating clinical practice guidelines into large language models for improved clinical reasoning. By transforming guidelines into executable decision logic, the model enhances its ability to generate accurate question-answering data. The results indicate a significant improvement in clinical reasoning accuracy and alignment with physician evaluations.
- ▪MedGuideX utilizes a guideline-derived training pipeline to enhance clinical decision-making in large language models.
- ▪The model achieved a 10.28% relative improvement in average accuracy across four clinical reasoning benchmarks.
- ▪Physician evaluations indicated that MedGuideX produces more faithful, valid, complete, and clear rationales compared to existing methods.
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Computer Science > Artificial Intelligence arXiv:2605.26567 (cs) [Submitted on 26 May 2026] Title:MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning Authors:Yuhao Shen, Lang Cao, Simo Du, Yuqing Wang, Juexiao Zhou, Hao Peng, Yue Guo View a PDF of the paper titled MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning, by Yuhao Shen and 6 other authors View PDF HTML (experimental) Abstract:Clinical practice guidelines (CPGs) encode evidence-based decision logic that clinicians apply by evaluating patient variables, conditional criteria, and recommendation rules.
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