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STAT+: In radiology, AI is blurring the line between technology development and clinical practice

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STAT+: In radiology, AI is blurring the line between technology development and clinical practice

A decade ago, machine learning scientist and Nobel laureate Geoffrey Hinton made a proclamation that still puts radiologists on edge.

“If you work as a radiologist,” the so-called godfather of artificial intelligence said at a conference, “you’re like a coyote that’s already over the edge of the cliff, but hasn’t yet looked down.” Deep learning was getting so good, so fast, said Hinton, that “people should stop training radiologists now.” In five years — ten, max — AI would do better than radiologists, he predicted. 

The clock has run out on that prediction. But the field of radiology isn’t just staring at its shoes, waiting to see how technology upends the profession. Instead, a growing number of radiology practices, in particular outpatient and teleradiology groups, are aggressively embracing AI: developing and acquiring their own tech, deploying it in-house, and marketing their “AI-native” capabilities to radiologist employees and hospital customers alike. 



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