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

Katie covers the impact of health technology on patients, clinicians, and businesses. Her stories explore the price tag of clinical AI, digital health at the FDA, and the boom in direct-to-consumer telehealth. Confidential tips can be sent on Signal at palmer.01.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. STAT+ Exclusive Story Already have an account? Log in This article is exclusive to STAT+ subscribers Unlock this article — and get additional analysis of the technologies disrupting health care — by subscribing to STAT+. Already have an account? Log in View All Plans To read the rest of this story subscribe to STAT+. Subscribe