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Artificial intelligence in healthcare


Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

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Fig. 1: Supervised and unsupervised machine learning.
Fig. 2: The general architecture of artificial neural networks.
Fig. 3: Models of information flow in conventional clinical practice, conventional decision support systems, integrative decision support systems and fully automated clinical systems.
Fig. 4: Example of the interpretation of convolutional neural networks.


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K.-H.Y. is supported by a Harvard Data Science Postdoctoral Fellowship. I.S.K. was supported in part by the NIH grant OT3OD025466. Figure 4 was generated by using the computational infrastructure supported by the AWS Cloud Credits for Research, the Microsoft Azure Research Award, and the NVIDIA GPU Grant Programme.

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K.-H.Y. conceived and designed the article, performed the literature review and wrote and revised the manuscript. A.L.B. and I.S.K. edited the manuscript. I.S.K. supervised the work.

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Correspondence to Isaac S. Kohane.

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Harvard Medical School (K.-H.Y.) submitted a provisional patent application on digital pathology profiling to the United States Patent and Trademark Office (USPTO).

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Yu, KH., Beam, A.L. & Kohane, I.S. Artificial intelligence in healthcare. Nat Biomed Eng 2, 719–731 (2018).

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