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Health Care Reform
December 17, 2018

Deep Learning in Medicine—Promise, Progress, and Challenges

Author Affiliations
  • 1Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York
  • 2Department of Medicine, Weill Cornell Medicine, New York, New York
JAMA Intern Med. 2019;179(3):293-294. doi:10.1001/jamainternmed.2018.7117

Recent years have seen a surge of interest in machine learning and artificial intelligence techniques in health care.1 Deep learning2 represents the latest iteration in a progression of artificial intelligence technologies that have allowed machines to mimic human intelligence in increasingly sophisticated and independent ways.3 Early medical artificial intelligence systems relied heavily on experts to train computers by encoding clinical knowledge as logic rules for specific clinical scenarios. More advanced machine learning systems train themselves to learn these rules by identifying and weighing relevant features from the data, such as pixels from medical images, or raw information from electronic health records (EHRs).

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