Reasoning LLMs in the Medical Domain: A Literature Survey

TL;DR

This survey reviews the evolution of medical Large Language Models, highlighting their reasoning capabilities, technological foundations, evaluation methods, and challenges to guide future development in clinical applications.

Contribution

It provides a comprehensive analysis of medical LLMs, focusing on reasoning techniques, evaluation strategies, and challenges, establishing a roadmap for reliable clinical deployment.

Findings

Advanced reasoning enhances decision transparency in medical LLMs

Prompting techniques like Chain-of-Thought improve clinical reasoning

Evaluation methodologies face challenges in validation and bias mitigation

Abstract

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.