How machine learning could revolutionize medicine

How machine learning could revolutionize medicine

How machine learning could revolutionize medicine
Doctors will one day be able to more accurately predict how long patients with fatal diseases will live. Medical systems will learn how to save money by skipping expensive and unnecessary tests. Radiologists will be replaced by computer algorithms.

These are just some of the realities patients and doctors should prepare for as “machine learning” enters the world of medicine, according to Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, and Dr. Ezekiel Emanuel of the University of Pennsylvania, who recently coauthored an article in the New England Journal of Medicine on the topic.

But what exactly is “machine learning”? And how will medical systems make use of it?

Obermeyer, who is also an emergency physician at Boston’s Brigham and Women’s Hospital, spoke with STAT to provide some answers. This discussion has been edited and condensed.

The traditional approach to solving problems with technology is to give the computer some rules and apply brute computing force. With machine learning, you don’t actually give machines rules. You give them data and ask them to learn the rules. We can point this very powerful tool at a medical problem and say, “I’m going to show you a bunch of people who had heart attacks, and a bunch who didn’t. Go learn how to tell them apart.” Then, once the algorithm has seen a million patients and what happened to them, you can show it information about a new patient and let it predict whether he might be at imminent risk for a heart attack.

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These algorithms are extraordinarily good at telling the difference. What we need to know more is, what are the rules the machine is learning, and how did it arrive at those rules? That’s sort of the next frontier of this.

That’s the really weird thing. With machine learning, whether in medical or other settings, you actually just have predictions.

Will doctors be willing to accept the conclusions of an algorithm without understanding how it achieved those conclusions?

That sort of thinking isn’t foreign to medicine. With something like the development of the hip replacement, it was sort of this engineered product that came out of a deep understanding of the mechanics of the hip. But in the history of medicine, there’s also a lot of things that make less sense. Think about the discovery of steroids for immune suppression, where medicine begins with a very pragmatic observation of, “Oh, this thing works,” and then goes to work in trying to backfill our understanding of why it works. That’ll be the model for a lot of machine learning applications.

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In 20 years, radiologists won’t exist in anywhere near their current form. They might look more like cyborgs: supervising algorithms reading thousands of studies per minute and zooming in to inspect and adjudicate ambiguous cases; or they might transform into “diagnosticians” like Dr. House, that go out and have more contact with patients and integrate that into their diagnostic judgments. Think about construction workers: They are still indispensable for construction — but they are doing very different jobs today than they were before mechanization 100 years ago.

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