Diagnosing medical conditions is among the more classic examples of actually useful, achievable real-world machine learning. Machines have data, lots of it, and they have the capacity to process all of that data in ways that humans can't. Crucially, machines should be able to pick up on-the-edge cases, the rarest diseases that may go undiagnosed for simple lack of experience on the part of even the most exceptional doctors. Here, machines are to augment humans, rather than replace them. To this end, a group of Chinese ophthalmologists and computer scientists has demonstrated a machine learning algorithm for identifying congenital cataracts, a rare eye disease that's nonetheless responsible for some 10 percent of all vision loss in children worldwide.

 

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