Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.
| Autor: | Maliha G; Perelman School of Medicine, University of Pennsylvania.; Department of Internal Medicine, University of Pennsylvania., Gerke S; Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, Harvard Law School, Harvard University., Cohen IG; Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, Harvard Law School, Harvard University.; Harvard Law School, Harvard University., Parikh RB; Perelman School of Medicine, University of Pennsylvania.; Department of Internal Medicine, University of Pennsylvania.; Penn Center for Cancer Care Innovation, University of Pennsylvania.; Corporal Michael J. Crescenz VA Medical Center. |
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| Jazyk: | angličtina |
| Zdroj: | The Milbank quarterly [Milbank Q] 2021 Sep; Vol. 99 (3), pp. 629-647. Date of Electronic Publication: 2021 Apr 06. |
| DOI: | 10.1111/1468-0009.12504 |
| Abstrakt: | Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability. While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence. Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no-fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care. (© 2021 Milbank Memorial Fund.) |
| Databáze: | MEDLINE |
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