Artificial Intelligence in Radiology: An Exciting Future, but Ethically Complex

Abstract

“Let me start by saying a few things that seem obvious. I think if you work as a radiologist, you’re like the coyote that’s already over the edge of the cliff but hasn’t yet looked down, so doesn’t know there’s no ground underneath him. People should stop training radiologists now. It’s just completely obvious that within 5 years, deep learning is going to do better than radiologists, because it’s going to be able to get a lot more experience. It might be 10 years, but we’ve got plenty of radiologists already. I said this at a hospital, and it didn’t go down too well.” With those words at a 2016 Creative Destruction Lab (CDL) seminar on ‘Machine Learning and the Market for Intelligence’ in Toronto, Canada, Dr Geoff Hinton provided radiologists the world over with an uncomfortable prediction of their obsolescence (and provided a piece of video that always gets attention from audiences during speeches about artificial intelligence [AI] and radiology). Dr Hinton, an English/Canadian cognitive psychologist and computer scientist, is, fittingly, the great-great-grandson of George Boole. There have been many other such predictions in recent years, some from sources that know less about the subject than Dr Hinton. In October 2020, the Dutch Finance Minister, Wopke Hoekstra, said: “The work of the radiologist to a significant extent has become redundant, because […] a machine can read the images better than humans who studied 10 years for it.” He also commented that the same changes were occurring with supermarket checkout operators.