Reasoning LLMs in the Medical Domain: A Literature Survey
It addresses the need for reliable LLMs to support clinical practice and medical research by enhancing decision transparency and explainability, though it is incremental as a literature survey.
This survey examines how large language models (LLMs) are evolving from basic information retrieval tools to sophisticated clinical reasoning systems in healthcare, focusing on enabling technologies like Chain-of-Thought prompting and Reinforcement Learning breakthroughs such as DeepSeek-R1.
The emergence of advanced reasoning capabilities in Large Language Models (LLMs) marks a transformative development in healthcare applications. Beyond merely expanding functional capabilities, these reasoning mechanisms enhance decision transparency and explainability-critical requirements in medical contexts. This survey examines the transformation of medical LLMs from basic information retrieval tools to sophisticated clinical reasoning systems capable of supporting complex healthcare decisions. We provide a thorough analysis of the enabling technological foundations, with a particular focus on specialized prompting techniques like Chain-of-Thought and recent breakthroughs in Reinforcement Learning exemplified by DeepSeek-R1. Our investigation evaluates purpose-built medical frameworks while also examining emerging paradigms such as multi-agent collaborative systems and innovative prompting architectures. The survey critically assesses current evaluation methodologies for medical validation and addresses persistent challenges in field interpretation limitations, bias mitigation strategies, patient safety frameworks, and integration of multimodal clinical data. Through this survey, we seek to establish a roadmap for developing reliable LLMs that can serve as effective partners in clinical practice and medical research.