How Machine Learning May 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.
How is machine learning different than, say, artificial intelligence?
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...
- Tags:
- artificial intelligence (AI)
- Bob Tedeschi
- Brigham and Women’s Hospital
- diagnostic test optimization
- electronic health records (EHRs)
- end-of-life care
- Ezekiel Emanuel
- Harvard Medical School
- machine learning
- New England Journal of Medicine
- radiologists
- remaining life span prediction
- University of Pennsylvania
- Ziad Obermeyer
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