In 2016, Geoffrey Hinton, Turing Award winner Computer Scientist said that ‘people should stop training radiologists now’.
And its going in that direction.
Radiology accounts for the vast majority of AI medical devices.
Radiology has >700 FDA cleared AI models that beat doctors on tests.
AI tools like CheXNet, trained on 100,000 chest X-rays, can diagnose pneumonia faster and more accurately than many doctors, taking less than a second per scan.
Despite this, hospitals still hire more radiologists, not fewer. In 2025, U.S. radiology residencies hit 1,208 openings, up 4% from the year before, and average pay jumped to $520,000, the second highest of any specialty.
This could be a classic example of Jevons paradox.
Jevons paradox means that when a process becomes cheaper, faster, or more efficient, people end up using more of it instead of less.
In radiology, AI and digital imaging made scanning faster and cheaper to process. Once turnaround times dropped from days to hours, hospitals started ordering more scans for more patients, since imaging became a low-friction diagnostic tool. So even though each radiologist could handle more cases, the overall number of scans ballooned, which created more work overall.
Why AI hasn’t replaced radiologists
Three main problems hold AI back. First, models that look great on benchmark datasets often collapse in real hospitals. Many are trained and tested on data from only one site, and their accuracy can drop 20% when moved elsewhere. Second, each tool only handles one condition, like a stroke or lung nodule, so doctors would need to juggle dozens of them to cover a typical day. Third, regulation and insurance rules still demand a human in the loop.
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worksinprogress. co/issue/the-algorithm-will-see-you-now/