Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
Qi Peng, Jiatong Li, Sirui Huang, Yiyang Jiang, Kaisong Gong, Ronger Ding, Shijie Ye, Changmeng Zheng, Yi Cai, Xiaobo Yang, Jin Huang, Xiao-Yong Wei, Qing Li
Why It Matters
What makes this one worth your time
This work is crucial for AI researchers and engineers aiming to develop reliable AI systems that meet clinical requirements and improve patient care.
A comprehensive survey linking clinical needs with AI capabilities in medical reasoning.
Summary
The paper surveys recent advancements in large language models (LLMs) for medical reasoning, presenting a dual-view framework that connects clinical competencies with computational reasoning patterns, and introduces a benchmark dataset to evaluate model performance across different reasoning levels.
Key contributions
- Establishment of a five-level competency scheme based on Miller's Pyramid for medical reasoning.
- Linking reasoning patterns (deductive, inductive, abductive) to medical goals and tasks.
- Introduction of a benchmark dataset and performance evaluation of 18 state-of-the-art models.
Notable insights
- The dual-view approach effectively bridges the gap between clinical competencies and computational reasoning, enhancing the understanding of LLM applications in healthcare.
- The introduction of a benchmark dataset for evaluating medical reasoning capabilities provides a valuable resource for future research.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.07761v1 Announce Type: new 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.