@inproceedings{chen-etal-2026-evolutionary,
title = "The Evolutionary Trajectory of Medical Multi-Agent Systems: A Survey",
author = "Chen, Kai and
Li, Chenzhang and
Yu, Tianyi and
Zuo, Changzhuo and
Zhou, Xinyun and
Jing, Sijia and
Wu, Yanqing and
Gao, Wenfei and
Li, Xinfeng and
Yang, Tianpei and
An, Bo and
Gao, Yang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1210/",
pages = "25530--25550",
ISBN = "979-8-89176-462-0",
abstract = "Breakthroughs in large language models are accelerating the evolution of medical artificial intelligence from standalone models to multi-agent systems. To organize this landscape, we propose a hierarchical taxonomy spanning Level 0 to Level 5. Given that foundational Level 0 to Level 2 architectures face cognitive and collaborative bottlenecks in complex clinical workflows, this survey focuses primarily on the architectural paradigm shift between Level 3 and Level 4. Specifically, we examine how systems transition from static Level 3 collaboration based on predefined roles to Level 4 self-evolving architectures that iteratively refine internal strategies through interactive feedback. Concurrently, we explore governance frameworks required to ensure system safety and trustworthiness under stringent clinical standards. Finally, we highlight that the capacity to dynamically generate agents and reconstruct collaborative topologies based on real-world scenarios will be essential for surpassing current capability limits. Building on this concept, we envision a Level 5 medical AI landscape defined by evolutionary agent swarms. To help researchers continuously track this field, our updated GitHub repository (https://github.com/Tiany1Yu/Medical{\_}Multi{\_}Agent{\_}Systems{\_}Survey{\_}Papers) is publicly available."
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<abstract>Breakthroughs in large language models are accelerating the evolution of medical artificial intelligence from standalone models to multi-agent systems. To organize this landscape, we propose a hierarchical taxonomy spanning Level 0 to Level 5. Given that foundational Level 0 to Level 2 architectures face cognitive and collaborative bottlenecks in complex clinical workflows, this survey focuses primarily on the architectural paradigm shift between Level 3 and Level 4. Specifically, we examine how systems transition from static Level 3 collaboration based on predefined roles to Level 4 self-evolving architectures that iteratively refine internal strategies through interactive feedback. Concurrently, we explore governance frameworks required to ensure system safety and trustworthiness under stringent clinical standards. Finally, we highlight that the capacity to dynamically generate agents and reconstruct collaborative topologies based on real-world scenarios will be essential for surpassing current capability limits. Building on this concept, we envision a Level 5 medical AI landscape defined by evolutionary agent swarms. To help researchers continuously track this field, our updated GitHub repository (https://github.com/Tiany1Yu/Medical_Multi_Agent_Systems_Survey_Papers) is publicly available.</abstract>
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%0 Conference Proceedings
%T The Evolutionary Trajectory of Medical Multi-Agent Systems: A Survey
%A Chen, Kai
%A Li, Chenzhang
%A Yu, Tianyi
%A Zuo, Changzhuo
%A Zhou, Xinyun
%A Jing, Sijia
%A Wu, Yanqing
%A Gao, Wenfei
%A Li, Xinfeng
%A Yang, Tianpei
%A An, Bo
%A Gao, Yang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-evolutionary
%X Breakthroughs in large language models are accelerating the evolution of medical artificial intelligence from standalone models to multi-agent systems. To organize this landscape, we propose a hierarchical taxonomy spanning Level 0 to Level 5. Given that foundational Level 0 to Level 2 architectures face cognitive and collaborative bottlenecks in complex clinical workflows, this survey focuses primarily on the architectural paradigm shift between Level 3 and Level 4. Specifically, we examine how systems transition from static Level 3 collaboration based on predefined roles to Level 4 self-evolving architectures that iteratively refine internal strategies through interactive feedback. Concurrently, we explore governance frameworks required to ensure system safety and trustworthiness under stringent clinical standards. Finally, we highlight that the capacity to dynamically generate agents and reconstruct collaborative topologies based on real-world scenarios will be essential for surpassing current capability limits. Building on this concept, we envision a Level 5 medical AI landscape defined by evolutionary agent swarms. To help researchers continuously track this field, our updated GitHub repository (https://github.com/Tiany1Yu/Medical_Multi_Agent_Systems_Survey_Papers) is publicly available.
%U https://aclanthology.org/2026.findings-emnlp.1210/
%P 25530-25550
Markdown (Informal)
[The Evolutionary Trajectory of Medical Multi-Agent Systems: A Survey](https://aclanthology.org/2026.findings-emnlp.1210/) (Chen et al., Findings 2026)
ACL
- Kai Chen, Chenzhang Li, Tianyi Yu, Changzhuo Zuo, Xinyun Zhou, Sijia Jing, Yanqing Wu, Wenfei Gao, Xinfeng Li, Tianpei Yang, Bo An, and Yang Gao. 2026. The Evolutionary Trajectory of Medical Multi-Agent Systems: A Survey. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 25530–25550, Budapest, Hungary. Association for Computational Linguistics.