Medical Reasoning in the Era of LLMs

This review categorizes techniques for enhancing LLM medical reasoning into training-time (e.g., fine-tuning, RL) and test-time (e.g., prompt engineering, multi-agent systems) approaches, applied across modalities and clinical tasks. It highlights advances in evaluation methods, key challenges like the faithfulness–plausibility gap, and the need for native multimodal reasoning in future medical AI.