Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.
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The authors would like to thank D. Wang, E. Dorfman, and A. Rajkomar for the visual design of the figures in this paper and P. Nejad for insightful conversation and ideas.
M.D., C.C., K.C., G.C. and J.D. are employees of Google Inc. This work was internally funded by Google Inc. G.C. is a board member at the Partnership on AI to Benefit People and Society. S.T. is an employee of Udacity, Inc. and the Kitty Hawk Corporation. He is on the faculty of Stanford University and Georgia Institute of Technology. B.R. is a partner of Computable LLC.
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Esteva, A., Robicquet, A., Ramsundar, B. et al. A guide to deep learning in healthcare. Nat Med 25, 24–29 (2019). https://doi.org/10.1038/s41591-018-0316-z
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