A risk-adaptive full lifecycle framework for medical AI agents: MAAM – MAAE

Medical artificial intelligence is evolving from single-output models into agents capable of maintaining contextual coherence, performing multi-step planning, invoking tools, and initiating or drafting clinical actions. Compared with traditional models, the risk of agents is no longer measured solely by whether a single output is correct; it depends more on how they perform task planning, invoke permissions, write to memory, select tools, and coordinate with the clinical team. Once an error propagates through any of these links, it may translate from a deviation at the level of recommendation into consequences at the level of action. Therefore, evaluation before medical agents enter clinical settings cannot rely only on the model’s own performance metrics; it must cover the complete system formed by the human–agent–workflow. The core question of this paper is: according to the severity of the consequences of medical agent actions, what level of system evidence is required to determine whether an agent can safely enter and continuously operate in clinical settings. Through a narrative review, this study integrates interdisciplinary literature from medical artificial intelligence, human factors engineering, safety engineering, cybersecurity, implementation science, health economics, reporting standards, and regulatory science, and constructs MAAM–MAAE, a risk-adaptive conceptual methodology. We map existing standards to agent-specific attributes and risks and organize unresolved requirements into interconnected R&D and evaluation cycles. It should be noted that this study uses purposive rather than systematic evidence synthesis and has not conducted a formal expert consensus process or prospective validation. Medical Agent Architecture and Maturation (MAAM) divides R&D activities into four domains: clinical mission, system architecture, human–agent collaboration (including action authority), and safety and maturity assurance. At the assessment level, MAAM introduces Medical Agent Assessment and Evaluation (MAAE) to organize evidence across four hierarchical dimensions: capability, safety, human–agent and clinical performance, and health system and full-lifecycle value. The unit of evaluation is the complete human–agent–workflow system. Four risk tiers and five stage gates—the gates are set according to potential harm, the physical proximity of actions to patients, the degree of autonomy, error detectability, and reversibility—so that supervision intensity matches the required level of evidence. In addition, this paper proposes clinical task success (CTS) and clinically consequential unsafe action rate (CCUAR) as dual anchoring metrics; however, their measurement properties and decision thresholds still require empirical testing. MAAM–MAAE transforms clinical authorization from a one-time model validation decision into a version-specific full-lifecycle contract. By incorporating actions, longitudinal operation, change control, and exit mechanisms, this framework complements existing reporting and regulatory frameworks. MAAM–MAAE is a testable proposal, not a validated checklist, reporting guideline, or claim of clinical effectiveness.

Key Words: medical agents; agentic artificial intelligence; clinical evaluation; human–medical agent collaboration; patient safety; full-lifecycle governance; methodology