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Maccaro A, Stokes K, Statham L, He L, Williams A, Pecchia L, Piaggio D. Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices. J Pers Med 2024; 14:443. [PMID: 38793025 PMCID: PMC11121798 DOI: 10.3390/jpm14050443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 05/26/2024] Open
Abstract
The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.
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Affiliation(s)
- Alessia Maccaro
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Katy Stokes
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Laura Statham
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Lucas He
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Faculty of Engineering, Imperial College, London SW7 1AY, UK
| | - Arthur Williams
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Leandro Pecchia
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Intelligent Technologies for Health and Well-Being: Sustainable Design, Management and Evaluation, Faculty of Engineering, Università Campus Bio-Medico Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Davide Piaggio
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
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Zaleski A, Thomas Craig KJ, Caddigan E, Yang H, Cheng Z, McNutt SL, Baquet-Simpson A. Leveraging Clinical Informatics to Address the Quintuple Aim for End-of-Life Care. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:784-793. [PMID: 38222390 PMCID: PMC10785881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
As the population of older adults grows at an unprecedented rate, there is a large gap to provide culturally tailored end-of-life care. This study describes a payor-led, informatics-based approach to identify Medicare members who may benefit from a Compassionate CareSM Program (CCP), which was designed to provide specialized care management services and support to members who have end-stage and/or life-limiting illnesses by addressing the quintuple aim. Potential participants are identified through machine learning models whereby nurse care managers then provide tailored outreach via telephone. A retrospective, observational cohort analysis of propensity-weighted Medicare members was performed to compare decedents who did or did not participate in the CCP. This program enhanced the end-of-life care experience while providing equitable outcomes regardless of age, gender, and geography and decreased inpatient (-37%) admissions with concomitant reduced (-59%) medical spend when compared to decedents that did not utilize the end-of-life care management program.
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Affiliation(s)
- Amanda Zaleski
- Clinical Evidence Development, Aetna Medical Affairs, CVS Health, Wellesley, MA, US
| | - Kelly J Thomas Craig
- Clinical Evidence Development, Aetna Medical Affairs, CVS Health, Wellesley, MA, US
| | - Eamon Caddigan
- Aetna Analytics & Behavior Change, CVS Health, New York, NY, US
| | - Hannah Yang
- Aetna Analytics & Behavior Change, CVS Health, New York, NY, US
| | - Zenon Cheng
- Aetna Analytics & Behavior Change, CVS Health, New York, NY, US
| | | | - Alena Baquet-Simpson
- Clinical Evidence Development, Aetna Medical Affairs, CVS Health, Wellesley, MA, US
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Mello MM, Shah NH, Char DS. President Biden's Executive Order on Artificial Intelligence-Implications for Health Care Organizations. JAMA 2024; 331:17-18. [PMID: 38032634 DOI: 10.1001/jama.2023.25051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
This Viewpoint discusses a recent executive order by US President Joe Biden about the development and implementation of AI, including the role of government vs the private sector and how the order may affect health care.
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Affiliation(s)
- Michelle M Mello
- Department of Health Policy, Stanford University School of Medicine, Stanford, California
- Stanford Law School and The Freeman Spogli Institute for International Studies, Stanford, California
| | - Nigam H Shah
- Departments of Medicine and Biomedical Data Science, and the Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, California
| | - Danton S Char
- Department of Anesthesiology, Stanford University School of Medicine, Stanford, California
- Stanford Center for Biomedical Ethics, Stanford, California
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Bakken S. Quantitative and qualitative methods advance the science of clinical workflow research. J Am Med Inform Assoc 2023; 30:795-796. [PMID: 37073766 PMCID: PMC10114099 DOI: 10.1093/jamia/ocad056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 03/22/2023] [Indexed: 04/20/2023] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Department of Biomedical Informatics and Data Science Institute, Columbia University, New York, New York, USA
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