The Landscape of Medical Agents: A Survey

Abstract:

Medical Agents are an emerging class of agentic systems deployed in clinical settings that operate
over multimodal, longitudinal data, maintain internal state, plan and adapt sequences of actions, and interact
with clinical information systems under governance constraints. They extend traditional medical artificial
intelligence (MedAI) beyond narrow diagnostic and predictive models toward workflow-centric architectures
that address persistent challenges such as administrative burden, fragmented workflows, and workforce strain.
In this paper, we (i) propose a functional definition and three-level developmental roadmap for Medical Agents,
linking architectural capabilities (planning, memory, tool use, long-horizon control) to degrees of workflow
integration and autonomy; (ii) map representative deployments across hospital departments and tasks, including
domain-specific agents and multi-agent hospital simulations; and (iii) synthesize cross-cutting challenges in
safety, robustness, fairness, evaluation, and governance, outlining research directions for advancing capabilities
under clinical constraints and achieving system-level impact. We argue that Medical Agents should be treated as
emerging infrastructure for learning health systems, whose value will be measured less by benchmark accuracy
than by reliable restructuring of clinical workflows.

Submission Type: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Inigo_Urteaga1

Submission Number: 8315