A Survey of LLM -based Agents in Medicine: How far are we from Baymax? (2025)

Abstract: Large Language Models (LLMs) are transforming healthcare through LLM-based agents that can understand and assist with medical tasks. This survey examines the architectures, applications, and challenges of LLM-based agents in medicine. We analyze key components including system profiles, clinical planning, medical reasoning frameworks, and external capacity enhancement. The survey covers major applications in clinical decision support, medical documentation, training simulations, and healthcare service optimization, along with evaluation frameworks and metrics. While these agents show promise in enhancing healthcare delivery, challenges remain in hallucination management, multimodal integration, implementation, and ethics. We conclude by highlighting future directions in medical reasoning, physical system integration, and training simulations, providing researchers and practitioners with a structured overview of the field’s current state and prospects.

## introduction large language models (llms) are changing the field of artificial intelligence with their strong capabilities in text understanding, generation, and reasoning. the development of llm-based agents has achieved notable success in many areas, from creative writing @xcite to complex decision-making (chai et al., 2025; wei et al., * wenxuan wang and zizhan ma equally contribute to this paper. in the medical domain, llm-based agents have improved several clinical tasks. recent work shows their use in diagnostic support @xcite , patient communication @xcite , and medical education @xcite . by combining llms with medical knowledge bases, clinical guidelines, and healthcare systems, these agents are designed to understand complex medical situations @xcite , offer evidence-based recommendations @xcite , and support healthcare delivery @xcite . while clinical tasks are our primary focus due to their prominence in current llm-based agent research, we also survey applications in data analytics, training, service optimization, and emerging non-clinical domains to reflect their broader medical potential. despite these advances, the field still faces several challenges, including implementation issues @xcite , safety concerns @xcite , and ethical considerations @xcite . addressing these challenges is essential for the safe and reliable integration of llm-based agents into clinical practice. therefore, a comprehensive review is needed to analyze the current status and future directions of llm-based agents in medicine. in this article, we provide a systematic review of llm-based agents in medicine, examining important research questions and future directions. we first discuss the architectures and methods, including system profile, external capacity enhancement, clinical planning, and medical reasoning in section 3. section 4 covers the various clinical and administrative application scenarios in which these agents are used. section 5 outlines the evaluation frameworks and metrics for assessing their performance in healthcare settings. finally, section 6 highlights key challenges and future research directions for improving the reliability, safety, and clinical integration of llm-based agents. this review analyzed 60 studies on llm-based medical agents published between 2022-2024, selected from major databases using healthcare airelated keywords. the initial search yielded 300 papers, narrowed to 80 after screening, with 60 meeting final inclusion criteria. ## background this section outlines the core differences between llms and llm-based agents and highlights the unique considerations required for deploying such agents in medicine. ## llm-based medical agent architecture llm-based agents in medicine require welldefined architectures to integrate complex clinical knowledge, facilitate medical decision-making, and ensure safe and effective deployment. this section presents a systematic overview of their architectural components, focusing on how these agents structure their operations to enhance clinical performance. ## application scenarios llm-based agents are applied in various areas of medicine. this section outlines the main application scenarios and provides a summary in ## evaluation and benchmarking evaluating llm-based medical agents is essential for confirming their reliability, safety, and clinical effectiveness. a comprehensive evaluation framework is required to measure performance across different medical tasks, identify limitations, and guide improvements for clinical applications. a summary of the evaluation metrics and benchmark categories is provided in ## discussions integrating large language model (llm)-based agents into medical workflows presents both challenges and opportunities. while previous work has achieved successes, the field remains in its early stages. several significant challenges persist, and many opportunities require further exploration to fully realize their potential in healthcare applications. the following sections discuss these challenges and opportunities. ## conclusion this survey examines llm-based agents in medicine, covering their architectures, applications, and challenges. while these agents enhance diagnostics, data analysis, and clinical workflows, issues remain in hallucination management, multimodal integration, and medical reasoning accuracy. future work should focus on real-time error correction, improved multimodal fusion, and hybrid reasoning to enhance reliability and clinical utility.