Radiology's AI Paradox: Better Machines, Busier Doctors

Since CheXNet's 2017 debut, AI has shown potential to surpass human radiologists in accuracy. However, despite advancements, AI's real-world application faces hurdles: generalization limitations, stringent regulations, and AI's replacement of only a fraction of a radiologist's tasks. Counterintuitively, demand for radiologists remains high, with salaries soaring. This is due to AI's poor performance outside standardized conditions, regulatory barriers, and the multifaceted nature of a radiologist's job. The article concludes that widespread AI adoption necessitates adapting societal rules, AI will boost productivity, but complete human replacement isn't imminent.