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      Precision Medicine, AI, and the Future of Personalized Health Care

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          Abstract

          The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

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          Most cited references93

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          The UK Biobank resource with deep phenotyping and genomic data

          The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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            High-performance medicine: the convergence of human and artificial intelligence

            Eric Topol (2019)
            The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
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              The potential for artificial intelligence in healthcare

              The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.
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                Author and article information

                Contributors
                Kevin.johnson@vumc.org
                Journal
                Clin Transl Sci
                Clin Transl Sci
                10.1111/(ISSN)1752-8062
                CTS
                Clinical and Translational Science
                John Wiley and Sons Inc. (Hoboken )
                1752-8054
                1752-8062
                12 October 2020
                January 2021
                : 14
                : 1 ( doiID: 10.1111/cts.v14.1 )
                : 86-93
                Affiliations
                [ 1 ] Department of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA
                [ 2 ] Department of Pediatrics Vanderbilt University Medical Center Nashville Tennessee USA
                [ 3 ] IBM Watson Health Cambridge Massachusetts USA
                [ 4 ] Department of Clinical Neurology Vanderbilt University Medical Center Nashville Tennessee USA
                Author notes
                [*] [* ] Correspondence: Kevin B. Johnson ( Kevin.johnson@ 123456vumc.org )

                Article
                CTS12884
                10.1111/cts.12884
                7877825
                32961010
                ca7983b2-f5ea-483d-9161-383822077e4e
                © 2020 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 11 June 2020
                : 11 August 2020
                Page count
                Figures: 2, Tables: 0, Pages: 8, Words: 7260
                Funding
                Funded by: IBM Watson Health
                Funded by: Vanderbilt University , open-funder-registry 10.13039/100006537;
                Categories
                Review
                Reviews
                Review
                Custom metadata
                2.0
                January 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.7 mode:remove_FC converted:11.02.2021

                Medicine
                Medicine

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