The Next Paradigm in Medical AI: A Survey of Agentic AI in Biomedicine

Abstract

Biomedical AI is increasingly shaped by policybound, multi-step clinical workflows and non-stationary, multimodal data and tools. In this setting, the field is moving beyond static predictors toward agentic systems, enabled by foundation models that can perceive, plan, and act under explicit oversight-a shift that motivates this survey. However, the field lacks a coherent account that defines biomedical agency, relates foundational model capabilities to agent behaviors, and traces the pathway from pre-training to domain-adapted, deployable systems. This survey offers such an account by synthesizing operational notions of agency and reviewing capability-centered perspectives that organize memory, planning, tool use, reflection, and dialogue around a perceive-plan-act loop. This survey situates these perspectives along the model-building pathway, from pretraining through post-training adaptation to the orchestration mechanisms that operationalize agents. We highlight safety and governance considerations for high-stakes settings, emphasizing the fidelity of process and reasoning, uncertainty and abstention, privacy and provenance, and human oversight. Taken together, this survey provides a structured synthesis of how recent work connects foundation models to governable biomedical agentic systems and distills the recurring challenges and directions identified in the literature for reliable, accountable deployment.

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