Statistical Modeling, Causal Inference, and Social Science
Geoffrey Hinton is a legendary computer scientist . . . Naturally, people paid attention when Hinton declared in 2016, “We should stop training radiologists now, it’s just completely obvious within five years deep learning is going to do better than radiologists.” The US Food and Drug Administration (FDA) approved the first AI algorithm for medical imaging that year and there are now more than 80 approved algorithms in the US and a similar number in Europe.
Yet, the number of radiologists working in the US has gone up, not down, increasing by about 7% between 2015 and 2019. Indeed, there is now a shortage of radiologists that is predicted to increase over the next decade. What happened? The inert AI revolution in radiology is yet another example of how AI has overpromised and under delivered. . . .
Radiology—the analysis of images for signs of disease—is a narrowly defined task that AI might be good at, but image recognition algorithms are often brittle and inconsistent. . . . only about 11% of radiologists used AI for image interpretation in a clinical practice. Of those not using AI, 72% have no plans to do so while approximately 20% want to adopt within five years. The reason for this slow diffusion is poor performance. . . .
