@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://aclanthology.org/2025.findings-acl.539/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T A Survey of LLM-based Agents in Medicine: How far are we from Baymax?
%A Wang, Wenxuan
%A Ma, Zizhan
%A Wang, Zheng
%A Wu, Chenghan
%A Ji, Jiaming
%A Chen, Wenting
%A Li, Xiang
%A Yuan, Yixuan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-survey
%X 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.
%R 10.18653/v1/2025.findings-acl.539
%U https://aclanthology.org/2025.findings-acl.539/
%U https://doi.org/10.18653/v1/2025.findings-acl.539
%P 10345-10359
Markdown (Informal)
[A Survey of LLM-based Agents in Medicine: How far are we from Baymax?](https://aclanthology.org/2025.findings-acl.539/) (Wang et al., Findings 2025)
ACL
- Wenxuan Wang, Zizhan Ma, Zheng Wang, Chenghan Wu, Jiaming Ji, Wenting Chen, Xiang Li, and Yixuan Yuan. 2025. A Survey of LLM-based Agents in Medicine: How far are we from Baymax?. In Findings of the Association for Computational Linguistics: ACL 2025, pages 10345–10359, Vienna, Austria. Association for Computational Linguistics.