AI has become deeply embedded in radiology, but rather than replacing doctors as once predicted, it is reshaping how they work. In 2016, AI pioneer Geoffrey Hinton forecast that radiologists would be replaced by computers within five years; instead, the profession is growing, with practitioner numbers expected to rise by at least 26% over the next three decades. Radiology remains medicine's leading field for AI adoption, making it a bellwether for how AI-assisted decision-making might spread across healthcare and beyond.

AI tools now draft reports, flag urgent scans and can detect abnormalities invisible to the human eye, with one analysis of 43 trials finding AI-assisted colonoscopies detect more polyps than conventional ones. This matters because human error in reading diagnostic images runs at 3–5%, contributing to roughly 40 million mistakes worldwide each year. The challenge now is combining AI's precision with human judgement: unlike rule-based alerts doctors have long been able to override, many AI systems are "black box" neural networks that don't explain their reasoning, forcing radiologists to learn when to trust or overrule decisions they cannot fully interrogate.