From Pilot Trap to Infrastructure: A Governance Framework for Clinical AI Institutionalization in Health Systems (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Despite increasing technical maturity, most clinical artificial intelligence (AI) systems remain confined to pilot or experimental settings, rarely achieving sustained integration into routine healthcare delivery. The persistence of this "pilot trap" is driven primarily by structural and institutional constraints rather than algorithmic performance limitations. </sec> <sec> <title>OBJECTIVE</title> To develop a governance framework that enables the transition of clinical artificial intelligence (AI) from project-based experimentation to durable institutional infrastructure, informed by the establishment of a provincial-level AI platform within a policy-oriented healthcare system in China. </sec> <sec> <title>METHODS</title> An 18-month real-world institutionalization process of the Hebei Provincial Clinical AI Platform was examined, encompassing the formation of a dedicated Medical AI laboratory, designation as a provincial engineering center, acquisition of regulatory authorizations, and deployment of structured clinical application pathways. Framework construction was grounded in systematic analysis of governance arrangements, policy legitimacy mechanisms, and translational implementation trajectories observed throughout the institutionalization process. </sec> <sec> <title>RESULTS</title> The framework comprises six interdependent modules encompassing institutional carrier formation, data and computational infrastructure, ethical and regulatory governance, interdisciplinary operational coordination, translational scaling and regional dissemination, and continuous evaluation. Implementation evidence indicates that governance architecture functions as a prerequisite to, rather than a consequence of, technical deployment. Organizational anchoring, external legitimacy, and coordinating capacity enable AI systems to operate as enduring institutional infrastructure rather than transient technological experiments. The framework reframes clinical AI from an algorithmic artifact to an embedded institutional capability, redirecting implementation logic from technical performance metrics toward governance maturity. </sec> <sec> <title>CONCLUSIONS</title> Sustainable clinical AI implementation is associated with governance-first rather than technology-first strategies. Effective institutionalization requires the concurrent establishment of organizational ownership, policy legitimacy, and coordinating mechanisms prior to large-scale deployment. Although derived from a policy-oriented healthcare context in China, the core governance functions demonstrate potential transferability across health systems, with institutional mechanisms varying by context while functional requirements remain comparatively stable. The framework offers an operational architecture for health systems seeking AI as infrastructure rather than episodic experimentation. </sec> <sec> <title>CLINICALTRIAL</title> NA. </sec>

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