With machine learning and AI in healthcare, can you speak the language?

As artificial intelligence and machine learning start to make their mark on healthcare in a big way, there's no shortage of hype. But there's also no small amount of uncertainty about just what it all means – literally. "We haven't settled on how to talk about this yet, and it's creating confusion in the market," said Leonard D'Avolio, assistant professor in the Brigham and Women's Division of General Internal Medicine and Primary Care (part of Harvard Medical School), and CEO of machine learning company Cyft. "If I describe what I do as cognitive computing, but a competitor describes what they do as AI or machine learning or data mining, it's hard to even understand what problems we are trying to solve." Because the problems that can be solved in healthcare with AI are numerous and notable, said Zeeshan Syed, director of the clinical inference and algorithms program at Stanford Health Care – whether it's better decision support at the bedside, better business intelligence for the C-suite or big-picture challenges such as managing care "across complex networks of providers for complex populations and complex diseases."