MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning
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 executable clinical decision logic and uses it to generate factual and counterfactual question-answering dat
The proliferation of advanced LLMs and the critical need for reliable clinical reasoning in healthcare are converging to drive innovation in internalizing complex medical guidelines.
This research addresses a key challenge in AI adoption for healthcare by enabling LLMs to interpret and apply structured medical knowledge more accurately, reducing errors and improving decision support.
LLMs can move beyond simple text-based retrieval of medical guidelines to truly 'understand' and operationalize procedural medical decision logic, leading to more trustworthy AI in clinical settings.
- · Healthcare AI developers
- · Hospitals and clinics
- · Patients
- · Medical data scientists
- · Traditional clinical decision support systems
LLMs gain a more robust capability for evidence-based clinical reasoning, improving diagnostic and treatment recommendations.
Increased trust in AI-driven medical advice leads to broader adoption of AI tools in patient care pathways.
The methodology could be extended to other structured decision-making fields, accelerating AI implementation in regulated industries.
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Read at arXiv cs.AI