AI's mechanism not a prerequisite to adoption in healthcare
Researchers at Stanford University have deployed a deep learning algorithm to diagnose potentially cancerous skin lesions as accurately as a certified dermatologist. Although researchers could not explain what criteria the algorithm used to class a lesion as cancerous or benign, it has proven to be highly accurate at diagnosing cancers from images including photographs, x-rays and MRI scans. As with US Food and Drug Administration-approved medicines whose biochemical mechanism is unknown, the opaqueness of the algorithm's technique is not necessarily a regulatory obstacle to its use, according to Nicholson Price, a health law scholar from the University of Michigan.
Experts say medical images, like photographs, x-rays, and MRIs, are a nearly perfect match for the strengths of deep-learning software, which has in the past few years led to breakthroughs in recognizing faces and objects in pictures.

Companies are already in pursuit. Verily, Alphabet's life sciences arm, joined forces with Nikon last December to develop algorithms to detect causes of blindness in diabetics. The field of radiology, meanwhile, has been dubbed the “Silicon Valley of medicine” because of the number of detailed images it generates.

Black-box medicine

Although the predictions by Thrun’s team were highly accurate, no one was sure exactly which features of a mole the deep-learning program used to classify it as cancerous or benign. The result is the medical version of what’s been termed deep learning’s “black box” problem.

Unlike more-traditional vision software, where a programmer defines rules—for example, a stop sign has eight sides—in deep learning the algorithm finds the rules itself, but often without leaving an audit trail to explain its decisions.

“In the case of black-box medicine, doctors can’t know what is going on because nobody does; it’s inherently opaque,” says Nicholson Price, a legal scholar from the University of Michigan who focuses on health law.