Rethinking Explainability in AI for Diagnostic Imaging

This chapter argues for a shift from explainability to empirical validation as the primary criterion for integrating AI into diagnostic imaging. The focus should be on accuracy, validation, and clinical integration, ensuring AI systems are rigorously tested and calibrated to meet real‑world medical needs rather than requiring them to produce explanations that may not meaningfully enhance clinician understanding.