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."
That not ideal. 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."
There are important distinctions between AI and machine learning, said Syed, and further differentiations with regard to how machine learning can be put to work.
"AI is basically getting computers to behave in a smart manner," he said. "You can do that either through curated knowledge, or through machine learning."
Curated knowledge means you could hardwire in data that, say, "if a temperature reading is above 102, someone has a fever. That's getting the computer to behave in an intelligent manner, but it's using existing knowledge embedded in the system."
With machine learning, on the other hand, computers "derives knowledge from the data, going beyond just the basic notion of AI, to uncover new insights."
In healthcare, machine learning has a big role to play, "coming up with new biomarkers and new understanding of how different parameters correlate with disease and disease onset," said Syed. "Predicting diseases. Preventing diseases. Treating diseases. Along that entire spectrum there is a role for machine learning."
That said, there are specific types of machine learning, and each has different approaches and applications.
