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    <abstract>This survey reviews LLM-based multi-agent systems for clinical and healthcare workflows, including diagnosis, triage, consultation, discharge, mental health, and EHR-linked decision support. We define AI hospitals as workflow-level clinical systems in which agents take explicit roles, hand off shared state, use EHR- or guideline-grounded tools, and operate with safety gates and audit-ready logs. We argue that these systems should be compared at the workflow level, rather than only by model components or end-task accuracy, because clinical action, evidence, and accountability are expressed through state transitions and handoffs. We organize the literature through a workflow-level taxonomy covering roles and handoffs, memory and evidence, tools, and reasoning, control, and escalation. We further synthesize major workflow settings and task families, introduce a four-layer evaluation stack spanning safety, process, outcome, and operations, and connect model capabilities to workflow observables relevant to deployment. Finally, we present Integration Readiness Levels (IRL1-IRL6), task-level instrumentation requirements, and recurring workflow failure modes as a practical framework for comparing, evaluating, and deploying clinical LLM agents and AI hospitals.</abstract>
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%0 Conference Proceedings
%T LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals
%A Yao, Zonghai
%A yu, Hong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yao-yu-2026-llm
%X This survey reviews LLM-based multi-agent systems for clinical and healthcare workflows, including diagnosis, triage, consultation, discharge, mental health, and EHR-linked decision support. We define AI hospitals as workflow-level clinical systems in which agents take explicit roles, hand off shared state, use EHR- or guideline-grounded tools, and operate with safety gates and audit-ready logs. We argue that these systems should be compared at the workflow level, rather than only by model components or end-task accuracy, because clinical action, evidence, and accountability are expressed through state transitions and handoffs. We organize the literature through a workflow-level taxonomy covering roles and handoffs, memory and evidence, tools, and reasoning, control, and escalation. We further synthesize major workflow settings and task families, introduce a four-layer evaluation stack spanning safety, process, outcome, and operations, and connect model capabilities to workflow observables relevant to deployment. Finally, we present Integration Readiness Levels (IRL1-IRL6), task-level instrumentation requirements, and recurring workflow failure modes as a practical framework for comparing, evaluating, and deploying clinical LLM agents and AI hospitals.
%U https://aclanthology.org/2026.acl-long.2123/
%P 45772-45793

Markdown (Informal)

[LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals](https://aclanthology.org/2026.acl-long.2123/) (Yao & yu, ACL 2026)

• LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals (Yao & yu, ACL 2026)

ACL

• Zonghai Yao and Hong yu. 2026. LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45772–45793, San Diego, California, United States. Association for Computational Linguistics.