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      Artificial intelligence and clinical anatomical education: Promises and perils

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          Dissecting racial bias in an algorithm used to manage the health of populations

          Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
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            Knowledge and Teaching: Foundations of the New Reform

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              The practical implementation of artificial intelligence technologies in medicine

              The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Anatomical Sciences Education
                Anatomical Sciences Ed
                Wiley
                1935-9772
                1935-9780
                October 08 2022
                Affiliations
                [1 ]Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Faculty of Medicine, Nursing and Health Sciences Monash University Clayton Victoria Australia
                [2 ]Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences Monash University Clayton Victoria Australia
                [3 ]Monash Nursing and Midwifery, Faculty of Medicine Nursing and Health Sciences Monash University Clayton Victoria Australia
                [4 ]Menzies School of Health Research Darwin Northern Territory Australia
                [5 ]Monash Bioethics Centre, Faculty of Arts Monash University Clayton Victoria Australia
                [6 ]Monash Data Futures Institute Monash University Clayton Victoria Australia
                [7 ]Faculty of Education Monash University Clayton Victoria Australia
                Article
                10.1002/ase.2221
                36030525
                0aa90ac0-f727-486d-8aa3-1da4959219db
                © 2022

                http://creativecommons.org/licenses/by-nc/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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