The rise of artificial intelligence throughout medicine, including in radiology, gets the literary equivalent of a diagnostic workup in the April 3 issue of The New Yorker.

Authored by the physician and Pulitzer-winning author Siddhartha Mukherjee, the essay is a wonderfully written must-read top to bottom for the medical imaging community.

Mukherjee speaks with, among other AI thought leaders, Geoffrey Hinton, a computer scientist at the University of Toronto. Mukherjee writes:

“Hinton is passionate about the future of deep-learning diagnosis, in part, because of his own experience. As he was developing [deep-learning] algorithms, his wife was found to have advanced pancreatic cancer. His son was diagnosed with a malignant melanoma, but then the biopsy showed that the lesion was a basal-cell carcinoma, a far less serious kind of cancer. ‘There’s much more to learn here,’ Hinton said, letting out a small sigh. ‘Early and accurate diagnosis is not a trivial problem. We can do better. Why not let machines help us?’”

Mukherjee also mulls the possibility that, as a result of machines learning more and more, humans will learn less and less.

“It’s the perennial anxiety of the parent whose child has a spell-check function on her phone: what if the child stops learning how to spell? The phenomenon has been called ‘automation bias,’” he writes. “When cars gain automated driver assistance, drivers may become less alert, and something similar may happen in medicine.”

And he covers the “AI will augment physicians, not replace them” base, although he doesn’t treat that scenario as the final word on the subject.

Read the whole thing: