Clinical Artificial Intelligence as a Sociotechnical System: Structural Failure Modes and Governance Requirements

<b>Background</b>: Clinical artificial intelligence systems increasingly shape healthcare delivery, yet many fail to produce sustained real world benefit despite acceptable retrospective performance. Existing evaluation paradigms emphasize algorithmic accuracy and discrimination while systematically underaccounting for the sociotechnical, operational, and economic structures within which clinical AI systems are deployed.&nbsp; <div> </div> <div> <b><br></b> </div> <div> <b>Objective</b>: To examine clinical artificial intelligence deployment failure through a sociotechnical systems lens and to articulate governance as a system level design requirement integrating workflows, documentation practices, reimbursement incentives, and institutional accountability.&nbsp; </div> <div> </div> <div> <b><br></b> </div> <div> <b>Methods</b>: We conducted a structured qualitative synthesis spanning sociotechnical systems theory, health informatics, health services research, and artificial intelligence governance literature. Recurrent deployment failure modes were identified using an inductive thematic approach and organized into a taxonomy. These failure modes were mapped to governance gaps across the clinical AI lifecycle and used to derive explicit system level requirements, evaluation pathways, and testable propositions.&nbsp; </div> <div> <b><br></b> </div> <div> <b>Results</b>: Five recurrent clinical AI deployment failure modes were identified: workflow incompatibility, documentation mediated data distortion, reimbursement driven behavioral adaptation, audit and monitoring blind spots, and diffusion of accountability. These failures arise from structural properties of healthcare delivery and interact with statistical degradation mechanisms such as covariate shift and calibration drift, producing compounding risk after deployment. A governance oriented deployment framework specifying actionable checkpoints across pre-deployment, deployment, and post-deployment phases is proposed.&nbsp; </div> <div> </div> <div> <b><br></b> </div> <div> <b>Conclusions</b>: Clinical artificial intelligence safety and effectiveness are emergent properties of coupled sociotechnical systems rather than attributes of isolated models. Governance frameworks that neglect operational workflows, economic incentives, and accountability structures are structurally insufficient. This work provides a transparent and testable foundation for governance oriented clinical AI deployment and motivates future formalization of governed adaptive decision systems. </div>