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Implementing Clinical Artificial Intelligence as a Continuous Lifecycle
ABSTRACT
Clinical artificial intelligence (AI) requires sustained integration within health care systems. AI performance and use may change following model updates, shifts in population characteristics and data distributions, changes to clinical pathways, and adaptation by health care professionals and patients. Implementation therefore extends across the clinical AI lifecycle. We identify four priorities for sustainable implementation: socio-technical integration within clinical workflows; lifecycle governance and accountability; continuous generation of evidence on clinical and system value; and longitudinal assessment of equity. These priorities require defined clinical objectives, accountable governance, prospective evaluation, post-deployment monitoring, and mechanisms for adaptation or withdrawal. Implementation science can support this process by examining interactions between AI technologies, users, organizations, and wider health care systems over time. A lifecycle approach provides a basis for integrating clinical AI safely, effectively, equitably, and sustainably within routine health care.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.