Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
This survey reviews recent progress in large language models (LLMs) for medical reasoning, proposing a dual-view framework that connects clinical practice (via a five-level Miller's Pyramid competency scheme) with computational reasoning patterns (deductive, inductive, abductive). It introduces a benchmark dataset spanning five levels and evaluates 18 models, finding that medical specialist models excel in diagnosis while general models lead in decision support and dialogue. Open challenges include data limitations, hallucination, and grounding issues.
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[Submitted on 8 Jul 2026]
Title:Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
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Abstract:Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care. This survey examines recent progress in medical LLMs, focusing on reasoning applications and requirements. We present a dual-view approach that connects clinical practice with computational methods. On the clinical side, we establish a five-level competency scheme following Miller's Pyramid, progressing from knowledge recall to dynamic case management. On the computational side, we link deductive, inductive, and abductive reasoning patterns to common medical goals and tasks. We also introduce a benchmark dataset spanning five levels of medical reasoning capability and report results on 18 state-of-the-art models, revealing that medical specialist models excel in diagnosis-centric tasks while general models lead in decision support and dialogue. We conclude by discussing current progress and open challenges, including data limitations, hallucination, and grounding issues, and outline directions toward safer, more reliable, and workflow-ready systems.
Comments: Accepted by Machine Intelligence Research
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.07761 [cs.AI]
(or arXiv:2607.07761v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.07761
arXiv-issued DOI via DataCite
Submission history
From: Qi Peng [view email] [v1] Wed, 8 Jul 2026 15:19:37 UTC (12,458 KB)
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