GraphWalker: Patient Analogy Meets Information Gain for Clinical Reasoning with Large Language Models
arXiv:2604.06684v2 Announce Type: replace Abstract: Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare. While large language models (LLMs) offer a promising paradigm via in-context demonstrations that requires no task-specific parameter updates, existing methods for reasoning by patient analogy in EHR settings suffer from three core limitations: (1) Perspective Limitation, where data-driven similarity misaligns with LLM reasoning needs while model-driven signals are constrained by limited clinical competence; (2) Cohort Awareness
The paper leverages recent advancements in large language models to address a critical challenge in healthcare: efficient and accurate clinical reasoning using electronic health records.
Improving clinical reasoning with AI can lead to more accurate diagnoses, personalized treatments, and reduced healthcare costs, transforming the efficiency of medical practice.
The application of LLMs directly to structured and unstructured EHR data for complex clinical decision support marks a significant step towards autonomous medical AI assistants.
- · Healthcare systems
- · AI research labs
- · LLM developers
- · Patients
- · Traditional clinical decision support systems
- · Inefficient diagnostic processes
Widespread adoption of LLM-powered clinical reasoning tools in hospitals and clinics.
A significant reduction in diagnostic errors and an acceleration of treatment pathways.
The emergence of AI systems capable of fully autonomous diagnosis and treatment plan generation, with human oversight focused on complex edge cases.
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Read at arXiv cs.LG