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Clinical artificial intelligence (AI) is moving from promising research to operational infrastructure. Algorithms now inform clinical data interpretation, risk prediction, triage, documentation and workflow. Yet the culture surrounding evaluation still centres largely on what happens before deployment, such as model development, internal validation, external testing and regulatory authorisation. Now, that framing is no longer sufficient. In medicine, approval has never been the endpoint. Drugs enter postmarketing surveillance. Laboratories operate under continuous quality control. Imaging systems are subject to routine quality assurance. Clinical AI should be treated no differently 1