When the algorithm passes the test but fails the patient.

Tens of thousands of AI diagnostic tools have been authorised for clinical diagnosis worldwide, justified primarily by benchmark accuracy (performance on a fixed, curated test dataset), a number that says little about what the tool will do at the bedside. The field rests on three flawed foundations: datasets that do not reflect the populations served, development pipelines that exclude clinicians, and regulatory pathways that mistake on-paper accuracy for evidence of benefit. Closing the gap requires global coordination and the political will to treat clinical validation as a prerequisite rather than an afterthought.