Machine learning could transform medicine. Should we let it?

In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data. The raw power of the technology has improved dramatically in recent years, and it's now used in everything from medical diagnostics to online shopping to autonomous vehicles. But deep learning tools also raise worrying questions because they solve problems in ways that humans can't always follow. If the connection between the data you feed into the model and the output it delivers is inscrutable--hidden inside a so-called black box--how can it be trusted? Among researchers, there's a growing call to clarify how deep learning tools make decisions--and a debate over what such interpretability might demand and when it's truly needed.