A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.

Demonstrating algorithmic accuracy does not establish that AI improves diagnosis in practice. This analysis shares a five-phase framework for evaluating medical AI, moving beyond fragmented lab-based testing toward real-world clinical benefit. It combines technical, operational, human-AI interaction, clinical, and real-world validation stages, with built-in checkpoints and fallback mechanisms for safety issues or updates. The result is intended to be an adaptable roadmap to help researchers, institutions, and regulators build trustworthy AI systems throughout their lifecycle.