Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
Published in Machine Intelligence Research, 2026
Recommended citation: 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, and Qing Li. (2026). Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning. Machine Intelligence Research. https://arxiv.org/abs/2607.07761
This survey connects clinical practice with computational methods through a dual-view framework. On the clinical side, it develops a five-level competency scheme based on Miller’s Pyramid. On the computational side, it links deductive, inductive, and abductive reasoning to common medical goals and tasks.
The work also introduces a five-level medical reasoning benchmark and evaluates 18 state-of-the-art models, highlighting complementary strengths and persistent gaps in diagnosis, decision support, dialogue, hallucination, and grounding.
Co-first author.