More Efficiency, More Demand

"Geoffrey Hinton, the Nobel Prize winning computer scientist who is often referred to as the "Godfather of AI", famously asserted in 2016 that, "People should stop training radiologists now. It's just completely obvious that in five years deep learning is going to do better than radiologists." The logical expectation would be that the number of radiologists should begin to decline over time as they begin to get replaced by AI."

"To answer this we need to look back to a 19th century English economist named William Stanley Jevons. The eponymous Jevons Paradox is the economic principle that increased efficiency in resource use can lead to an overall increase in total resource consumption, rather than a decrease. Jevons noticed that improved steam engine efficiency resulted in higher coal consumption rather than less. He argued that as the efficiency of steam engines increased, their usage expanded, offsetting any gains made in energy conservation."

"Jevons noticed something the rest of the world kept missing. When technology makes a task cheaper or easier, we don't do less of it. We do more. The easier it becomes to extract value from a resource, the more we find reasons to use it. Coal was not conserved by better engines; it became the fuel of an expanding industrial world."

There were 30,723 radiologists in 2014 and 36,024 in 2023, with projections rising to 47,119 by 2055. Early predictions claimed deep learning would outperform and replace radiologists within years, suggesting workforce decline. Contrary to those predictions, the radiologist workforce increased, indicating that technological efficiency did not reduce demand. Jevons Paradox posits that improved efficiency in resource use can increase total resource consumption; improved steam engines increased coal use historically. When AI lowers the cost and effort of imaging interpretation, utilization expands, creating more clinical demand and new workflows requiring human oversight and integration.

Read at Substack