Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Science & Research — Health & Medical Research, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

A comprehensive survey examines recent progress in Large Language Models (LLMs) for medical reasoning, focusing on their application in clinical practice. It introduces a dual-view approach, connecting clinical needs with computational methods, and establishes a five-level competency scheme extending Miller's Pyramid from knowledge recall to dynamic case management. The survey also presents a new benchmark dataset comprising 5,000 curated samples, with 1,000 samples for each reasoning level. Evaluation of 18 state-of-the-art models on this benchmark reveals that medical specialist models excel in diagnosis-centric tasks, while general LLMs perform better in decision support and dialogue. The analysis highlights ongoing challenges, including data limitations, hallucination, and grounding issues, while outlining directions for safer, more reliable, and workflow-ready systems.

Key takeaway

For AI Scientists and Research Scientists developing medical LLMs, you should adopt a hybrid deployment strategy. Route diagnosis-centric tasks to specialized medical models, which demonstrate superior performance in these areas. Conversely, leverage high-capacity general LLMs for decision support, multi-turn dialogue, and summarization, where they excel in execution robustness and narrative flow. This approach optimizes performance across diverse clinical needs while mitigating risks associated with model limitations.

Key insights

Medical LLM capabilities are best understood and evaluated through a dual-view framework linking clinical competencies and reasoning types.

Principles

• Miller's Pyramid extends to a five-level competency hierarchy for medical LLMs.
• Medical reasoning involves deductive, inductive, abductive, and mixed patterns.
• Model size alone does not guarantee performance; instruction quality and domain data are key.

Method

A dual-view framework links a five-level clinical competency hierarchy (extending Miller's Pyramid) with computational reasoning types (deductive, inductive, abductive) to evaluate medical LLMs.

In practice

• Route diagnosis-heavy queries to specialized medical LLMs.
• Use general LLMs for decision support, dialogue, and summarization.
• Prioritize instruction quality and domain-specific data in LLM training.

Topics

• Medical LLMs
• Clinical Reasoning
• AI Evaluation Benchmarks
• Miller's Pyramid
• Diagnostic AI
• Retrieval-Augmented Generation

Best for: AI Scientist, Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.