Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.

Journal: The Milbank quarterly
Published Date:

Abstract

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.

Authors

  • George Maliha
    Perelman School of Medicine, University of Pennsylvania.
  • Sara Gerke
    The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School; The Project on Precision Medicine, Artificial Intelligence, and the Law, Cambridge, MA, USA.
  • I Glenn Cohen
    Harvard Law School, Cambridge, Massachusetts, United States of America.
  • Ravi B Parikh
    Division of Hematology and Oncology, Perelman School of Medicine, University of Philadelphia, Philadelphia, Pennsylvania.