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

Abstract

Artificial intelligence (AI) has advanced rapidly across diagnostic, prognostic, and clinical decision-support applications, yet the pathway from laboratory performance to demonstrable clinical benefit remains fragmented and inconsistently defined. Existing evaluations rely heavily on retrospective testing and algorithm-centric metrics, while current guidelines emphasize reporting standards rather than specifying validation across stages of model maturity. This study proposes a five-phase evaluation framework for medical AI, supported by a dynamic evaluation architecture reflecting the nonlinear, iterative nature of AI systems. The framework integrates technical validation, operational robustness validation, controlled interaction validation, clinical evidence validation, and real-world integration validation, while incorporating phase-gating criteria and local and systemic fall-back triggers. These mechanisms enable re-entry into earlier phases based on drift, version updates, or safety signals, and accommodate parallel activities such as implementation research informing clinical trials. By systematically mapping multicenter external validation, shadow-mode testing, human-AI comparison and cooperation studies, randomized controlled trials, real-world evaluations, and adaptive designs into a coherent lifecycle pathway, the framework addresses persistent gaps between laboratory performance and clinical benefit. It provides researchers, clinical institutions, and regulators with an operational, scalable approach aligned with evolving regulatory expectations, supporting trustworthy, ethically aligned, and lifecycle-based evidence generation for medical AI systems.