Artificial intelligence (AI) and deep learning are entering the mainstream of clinical medicine. For example, in December 2016, Gulshan et al 1 reported development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. An accompanying editorial by Wong and Bressler 2 pointed out limits of the study, the need for further validation of the algorithm in different populations, and unresolved challenges (eg, incorporating the algorithm into clinical work flows and convincing clinicians and patients to "trust a 'black box'"). Sixteen months later, the Food and Drug Administration (FDA) 3 permitted marketing of the first medical device to use AI to detect diabetic retinopathy. FDA reduced the risk of releasing the device by limiting the indication for use to screening adults who do not have visual symptoms for greater than mild retinopathy, to refer them to an eye care specialist.This issue of JAMA contains 2 Viewpoints on deep learning in health care. Hinton 4 explains the technology underlying AI and deep learning, using clinical examples. AI is the general term for imitating human intelligence with computer systems. Early AI systems represented human reasoning with symbolic logic. As computer processing and storage became more powerful, researchers developed machine-learning techniques to imitate the way the human brain learns. The first machine learning continued to rely on human experts to label the data the system trained on (eg, the diagnosis) and to identify the significant features (eg, findings). Machine learning weighted the features from the data. With continued advances in computational power and with larger data sets, researchers began to develop deep learning techniques. The first deep learning algorithms were "supervised" in that human experts continued to label the training data, and the deep learning algorithms learned the features and weights directly from the data. The retinopathy screening algorithms are an example of supervised deep learning. Hinton 4 describes continuing development of new deep learning techniques, including ones that are completely unsupervised. He also points out that it is not feasible to see the features learned by deep learning to explain how the system reaches a conclusion.Naylor 5 identifies 7 factors driving adoption of AI and deep learning in health care: (1) the strengths of digital imaging over human interpretation; (2) the digitization of health-related records and data sharing; (3) the adaptability of deep learning to analysis of heterogeneous data sets; (4) the capacity of deep learning for hypothesis generation in research; (5) the