MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning

📰 ArXiv cs.AI

Learn how MedGuideX internalizes decision logic from clinical guidelines into large language models for improved clinical reasoning

advanced Published 27 May 2026

Action Steps

Transform clinical practice guidelines into executable recommendations using MedGuideX
Train large language models on these executable guidelines to internalize decision logic
Evaluate the performance of the trained models on clinical reasoning tasks
Fine-tune the models using additional clinical data to improve accuracy
Deploy the trained models in clinical settings to support decision-making

Who Needs to Know This

Data scientists and clinicians on a team can benefit from this research to improve clinical decision-making and develop more accurate large language models for healthcare applications

Key Insight

💡 Internalizing decision logic from clinical guidelines into large language models can improve clinical reasoning and decision-making

Key Takeaways

Learn how MedGuideX internalizes decision logic from clinical guidelines into large language models for improved clinical reasoning

Full Article

Title: MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning

Abstract:
arXiv:2605.26567v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode evidence-based decision logic that clinicians apply by evaluating patient variables, conditional criteria, and recommendation rules. However, existing methods often use CPGs as free-text training data or retrieval sources, underutilizing their procedural decision structure. To better exploit this structure, we introduce a guideline-derived training pipeline that transforms CPG recommendations into executab

Read full paper → ← Back to Reads