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      Ethical implications of AI and robotics in healthcare: A review

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          Abstract

          Integrating Artificial Intelligence (AI) and robotics in healthcare heralds a new era of medical innovation, promising enhanced diagnostics, streamlined processes, and improved patient care. However, this technological revolution is accompanied by intricate ethical implications that demand meticulous consideration. This article navigates the complex ethical terrain surrounding AI and robotics in healthcare, delving into specific dimensions and providing strategies and best practices for ethical navigation. Privacy and data security are paramount concerns, necessitating robust encryption and anonymization techniques to safeguard patient data. Responsible data handling practices, including decentralized data sharing, are critical to preserve patient privacy. Algorithmic bias poses a significant challenge, demanding diverse datasets and ongoing monitoring to ensure fairness. Transparency and explainability in AI decision-making processes enhance trust and accountability. Clear responsibility frameworks are essential to address the accountability of manufacturers, healthcare institutions, and professionals. Ethical guidelines, regularly updated and accessible to all stakeholders, guide decision-making in this dynamic landscape. Moreover, the societal implications of AI and robotics extend to accessibility, equity, and societal trust. Strategies to bridge the digital divide and ensure equitable access must be prioritized. Global collaboration is pivotal in developing adaptable regulations and addressing legal challenges like liability and intellectual property. Ethics must remain at the forefront in the ever-evolving realm of healthcare technology. By embracing these strategies and best practices, healthcare systems and professionals can harness the potential of AI and robotics, ensuring responsible and ethical integration that benefits patients while upholding the highest ethical standards.

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          Most cited references49

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          World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.

          (2013)
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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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              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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                Author and article information

                Contributors
                Journal
                Medicine (Baltimore)
                Medicine (Baltimore)
                MD
                Medicine
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0025-7974
                1536-5964
                15 December 2023
                15 December 2023
                : 102
                : 50
                : e36671
                Affiliations
                [a ] University of California, Santa Cruz, CA
                [b ] Igbinedion University, Okada, Nigeria
                [c ] Imo State University, Owerri, Nigeria
                [d ] Kazan State Medical University, Kazan, Russia
                [e ] Chukwuemeka Odumegwu Ojukwu University Teaching Hospital, Awka, Nigeria
                [f ] Babcock University, Ilishan-Remo, Nigeria
                [g ] Luhansk State Medical University, Luhansk, Ukraine.
                Author notes
                [* ]Correspondence: Chukwuka Elendu, University of California, Santa Cruz, CA 95064 (e-mail: elenduchukwuka@ 123456yahoo.com ).
                Author information
                https://orcid.org/0000-0002-0249-1865
                Article
                00101
                10.1097/MD.0000000000036671
                10727550
                38115340
                7dfb08cc-3110-45d7-9136-b7f363ee7ad2
                Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 July 2023
                : 8 October 2023
                : 23 November 2023
                Categories
                5400
                Research Article
                Narrative Review
                Custom metadata
                TRUE

                algorithmic bias,artificial intelligence (ai),data security,ethical considerations,healthcare,privacy,robotics

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