Why AI will not replace radiologists – Towards Data Science

In late 2016 Prof Geoffrey Hinton, the godfather of neural networks, said that it's "quite obvious that we should stop training radiologists" as image perception algorithms are very soon going to be demonstrably better than humans. Radiologists are, he said, "the coyote already over the edge of the cliff who hasn't yet looked down". This kick-started a hype-wave of biblical proportions that is still rolling to this day, and shows no signs of breaking just yet. In my opinion, although this wave of enthusiasm and optimism has successfully brought radiology artificial intelligence to the forefront of people's imaginations, and immense amounts of funding with it, it has also done untold harm by over-inflating the expectations of policy and decision makers, and is having tangible knock-on effects on recruitment as disillusioned junior doctors start believing that machines are indeed replacing humans and so they shouldn't bother applying to become radiologists. It is hard to imagine a more damaging statement occurring at a time when the crisis in radiology staffing, especially acute in the UK, is threatening to destabilise entire hospital systems.