Machine learning in medical imaging

technology

The clinical domain with the longest record of confident prediction and the best randomised evidence, and the two do not agree. In 2016 Geoffrey Hinton publicly advised that training of radiologists should stop, on the view that deep learning would supersede human image reading within about five years. What the evidence since actually shows is narrower and better: in Sweden's MASAI trial, a randomised, population-based screening study of over 100,000 women, AI-supported reading raised the screen-detected cancer detection rate by 29% and cut screen-reading workload by 44.2% against standard double reading, with seven more false positives across the arms; the full results published in The Lancet reported a non-inferior interval-cancer rate, higher sensitivity, unchanged specificity, and fewer interval cancers with unfavourable characteristics. The workforce moved the other way from the prediction: the American College of Radiology's February 2025 workforce study projects growth, and United States employment projections for the occupation exceed the all-occupation average. A tool that reads alongside a radiologist and removes half the reading load is a substantial change; it is not the change that was announced.

A technology is not a place. Drawing it on a map would assert something about the world that no stored fact supports.