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      Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans

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          Abstract

          Background

          Despite widespread agreement that artificial intelligence (AI) offers significant benefits for individuals and society at large, there are also serious challenges to overcome with respect to its governance. Recent policymaking has focused on establishing principles for the trustworthy use of AI. Adhering to these principles is especially important for ensuring that the development and application of AI raises economic and social welfare, including among vulnerable groups and veterans.

          Objective

          We explore the newly developed principles around trustworthy AI and how they can be readily applied at scale to vulnerable groups that are potentially less likely to benefit from technological advances.

          Methods

          Using the US Department of Veterans Affairs as a case study, we explore the principles of trustworthy AI that are of particular interest for vulnerable groups and veterans.

          Results

          We focus on three principles: (1) designing, developing, acquiring, and using AI so that the benefits of its use significantly outweigh the risks and the risks are assessed and managed; (2) ensuring that the application of AI occurs in well-defined domains and is accurate, effective, and fit for the intended purposes; and (3) ensuring that the operations and outcomes of AI applications are sufficiently interpretable and understandable by all subject matter experts, users, and others.

          Conclusions

          These principles and applications apply more generally to vulnerable groups, and adherence to them can allow the VA and other organizations to continue modernizing their technology governance, leveraging the gains of AI while simultaneously managing its risks.

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          Most cited references23

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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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            Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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              Artificial intelligence in healthcare

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                Author and article information

                Contributors
                Journal
                JMIR Med Inform
                JMIR Med Inform
                JMI
                JMIR Medical Informatics
                JMIR Publications (Toronto, Canada )
                2291-9694
                June 2021
                2 June 2021
                : 9
                : 6
                : e28921
                Affiliations
                [1 ] National Artificial Intelligence Institute Department of Veterans Affairs Washington, DC United States
                [2 ] Stanford Digital Economy Lab Stanford University Stanford, CA United States
                [3 ] WP Carey School of Business Arizona State University Tempe, AZ United States
                [4 ] Office of Research & Development Department of Veterans Affairs Washington, DC United States
                [5 ] Boston Children’s Hospital Harvard Medical School Boston, MA United States
                Author notes
                Corresponding Author: Christos Makridis christos.makridis@ 123456va.gov
                Author information
                https://orcid.org/0000-0002-6547-5897
                https://orcid.org/0000-0002-2137-359X
                https://orcid.org/0000-0003-3621-4679
                https://orcid.org/0000-0002-0495-7059
                Article
                v9i6e28921
                10.2196/28921
                8209529
                34076584
                ab0bb3cc-9a2c-41ea-b16a-2f9c1604ae47
                ©Christos Makridis, Seth Hurley, Mary Klote, Gil Alterovitz. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.06.2021.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

                History
                : 18 March 2021
                : 3 April 2021
                : 23 April 2021
                : 27 April 2021
                Categories
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                artificial intelligence,ethics,veterans,health data,technology,veterans affairs,health technology,data

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