From Technical Performance to Clinical Readiness: A Phase-Based Framework for Evidence Standards in Clinical...

AbstractClinical AI is moving rapidly from technical development to patient care, yet evidence standards remain inconsistent. Many studies emphasize model discrimination and retrospective validation, while fewer assess calibration, transportability, prospective clinical impact, implementation readiness, or postdeployment safety. We propose a phase-based framework for evidentiary maturity in clinical AI, using cardiovascular AI as a representative use case. The framework spans model development and representation learning, internal validation, external validation, prospective evaluation of decision impact and implementation readiness, and postdeployment monitoring. Each phase is linked to a core question, expected evidence, and the type of claim that can reasonably be supported. By aligning claims with phase-appropriate evidence, this framework is intended to help investigators, reviewers, regulators, health systems, and clinicians distinguish technical feasibility from clinical readiness and identify when additional prospective evaluation, monitoring, or governance is needed.