Reasoning LLMs in the Medical Domain: A Literature Survey

2025-08-26

AI Summary

This paper addresses critical limitations of large language models (LLMs) in healthcare—namely, weak clinical reasoning, poor interpretability, unmitigated bias, absence of patient safety mechanisms, and challenges in multimodal data integration. To this end, we propose an evolutionary reasoning paradigm for clinical decision support. Our methodology integrates chain-of-thought prompting, a healthcare-specialized multi-agent architecture, reinforcement learning–based trustworthy reasoning optimization (e.g., inspired by DeepSeek-R1), multimodal clinical data fusion, and a domain-customized evaluation framework. Key contributions include: (1) the first systematic roadmap for advancing LLM reasoning capabilities in clinical settings; (2) identification of structural deficiencies in existing frameworks regarding clinical trustworthiness and safety alignment; and (3) a synergistic technical pathway for enhancing interpretability and mitigating bias, significantly improving model robustness and decision reliability in real-world clinical applications—thereby providing both theoretical foundations and practical paradigms for high-stakes clinical AI deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Enhancing decision transparency and explainability in medical LLMs
• Transforming medical LLMs into sophisticated clinical reasoning systems
• Addressing challenges in medical validation and bias mitigation strategies

Innovation

• Specialized prompting techniques like Chain-of-Thought
• Reinforcement Learning breakthroughs exemplified by DeepSeek-R1
• Multi-agent collaborative systems and innovative prompting architectures