Agentic health systems: from smart hospitals to continuously coordinated care

Medical artificial intelligence is evolving from single-task prediction models and generative assistants into agents capable of perceiving the environment, decomposing goals, invoking tools, coordinating resources, and tracking outcomes. However, a leap in capability does not equate to a leap in clinical authorization. Existing evidence comes mainly from retrospective data and simulated cases, while real-world, multicenter studies with patient outcomes as endpoints remain scarce. If autonomy is pursued without boundaries, accountability, and operational monitoring, agents may amplify a single model error into continuous harm across processes and institutions. This paper proposes the agentic health system (AHS) framework: under the premises of a clearly defined intended use, action authority, accountable entities, and failure fallback, agents execute a “perceive–prioritize–coordinate–act–learn” loop around patient goals, connecting the three domains of hospital, home, and public health, and shifting healthcare from fragmented services to continuous coordination. This paper further proposes four levels of action authority, a minimum implementation unit, a four-stage translation pathway, two anchor endpoints—system safety and system benefit—and the SCORE evaluation framework. The maturity criterion for AHS is not the number of automated tasks, but whether appropriate care can be delivered at the appropriate time, in the appropriate place, and under appropriate supervision, while improving safety, continuity, operational value, resilience, and equity. The endpoint of hospital agents should not be the “unmanned hospital,” but rather a supervised autonomous healthcare system that is professional-led, patient-engaged, machine-auditable, and accountability-traceable.