@inproceedings{wang-etal-2025-survey,
    title = "A Survey of {LLM}-based Agents in Medicine: How far are we from Baymax?",
    author = "Wang, Wenxuan  and
      Ma, Zizhan  and
      Wang, Zheng  and
      Wu, Chenghan  and
      Ji, Jiaming  and
      Chen, Wenting  and
      Li, Xiang  and
      Yuan, Yixuan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.539/",
    pages = "10345--10359",
    ISBN = "979-8-89176-256-5",
    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."
}