LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals

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 synthe-size 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. 1