Governing Adaptive Clinical Artificial Intelligence: Structural Failure Modes, Auditability, and Infrastructure for Decision Safety

Abstract

This collection reflects a structured research program examining clinical artificial intelligence as an adaptive sociotechnical infrastructure requiring explicit governance constraints. The included works develop a layered analytical framework addressing structural failure modes, deployment level safety considerations, reimbursement driven behavioral incentives, and externally constrained learning architectures. Across these contributions, the Externally Governed Learning Systems (EGLS) framework is introduced as a formal model for separating adaptive computation from institutional decision authority and viability enforcement. The materials in this collection collectively explore how governance mechanisms can be embedded at the infrastructure level to support auditability, reproducibility, and deployment safety in clinical AI systems. The intended audience includes clinicians, health informaticians, machine learning researchers, regulators, policymakers, and institutional leaders engaged in the deployment and oversight of adaptive AI systems in healthcare.

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