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A guide to deep learning in healthcare

Nature Medicine volume 25, pages 24–29 (2019)Cite this article

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Abstract

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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Fig. 1: Deep learning.
Fig. 2: Medical Imaging.
Fig. 3: Making predictions using EHRs.
Fig. 4: ML in genomics.

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Acknowledgements

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.

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Author notes

  1. These authors contributed equally: Andre Esteva, Alexandre Robicquet.

Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov & Sebastian Thrun

  2. Google Research, San Jose, CA, USA

    Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado & Jeff Dean

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  1. Andre Esteva
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Contributions

B.R., V.K., M.D., and K.C. share second authorship. C.C., G.C., and S.T. share third authorship. J.D. is the principal investigator. A.E. and A.R. conceptualized the structure of the review and contributed to the computer vision and reinforcement learning sections. V.K., B.R., and M.D. contributed to the generalized deep learning section. K.C. and J.D. contributed to the natural language processing section. C.C., G.C., S.T., and J.D. oversaw the work. All authors contributed to multiple parts of the review, as well as the style and overall contents.

Corresponding author

Correspondence to Andre Esteva.

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Competing interests

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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