Industry–academic structural alignment as a determinant of clinical AI translation: A five-domain conceptual model

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Abstract
Artificial intelligence (AI) tools in health care often demonstrate technical validity yet fail to achieve sustained integration within academic health centers. This persistent gap between pilot implementation and routine use suggests that translational barriers extend beyond model performance and regulation to structural differences between industry and health systems. We conducted an exploratory thematic synthesis of a multi-sector panel convened by the Clinical and Translational Science Awards (CTSA) Principles for AI Translation in Healthcare (PATH) Working Group, including representatives from industry and academic leadership. Recurrent tensions emerged across economic sustainability, liability allocation, data governance, infrastructure readiness, and lifecycle monitoring. From these themes, we propose a five-domain Industry–Academic Alignment Conceptual Model for clinical AI translation. The model conceptualizes translation as dependent on alignment across economic, risk and liability, data governance, infrastructure, and lifecycle monitoring domains, each reflecting differences in incentives, governance, and operational capacity. Misalignment across domains may contribute to stagnation at the pilot stage despite technical validity. This work frames clinical AI translation as an inter-organizational alignment problem and identifies multidomain alignment as a potentially important condition for sustained clinical integration.

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