We Have Misunderstood What Radiologists Do

AI is now used in 70% of UK radiology departments, yet only 28% of clinical directors report productivity gains from its implementation. This apparent disconnect may reflect a focus on how algorithms perform in isolation, rather than what happens when they are introduced into clinical practice. This feature examines evidence from mammography screening, stroke and lung cancer pathways alongside research on implementation, automation bias, deskilling, and accountability. It argues that the clinical value of AI depends on whether it changes decisions, uses clinical time effectively, and improves patient care without creating new risks or unowned work. As AI becomes embedded in routine practice, developers and healthcare organisations will need to stop treating diagnostic accuracy as a proxy for clinical value and demonstrate how their tools improve care within clinical pathways. Radiologists, meanwhile, must become sceptical adopters who can appraise evidence critically, recognise where local conditions may limit performance and help govern how machine outputs influence patient care.