When AIs Outperform Doctors: Confronting the Challenges of a Tort-Induced Over-Reliance on Machine Learning

67 Pages Posted: 13 Feb 2018 Last revised: 6 Mar 2019

See all articles by A. Michael Froomkin

A. Michael Froomkin

University of Miami - School of Law; Yale University - Yale Information Society Project

Ian R. Kerr

University of Ottawa - Common Law Section

Joelle Pineau

McGill University

Date Written: February 20, 2019

Abstract

Someday, perhaps soon, diagnostics generated by machine learning (ML) will have demonstrably better success rates than those generated by human doctors. What will the dominance of ML diagnostics mean for medical malpractice law, for the future of medical service provision, for the demand for certain kinds of doctors, and—in the long run—for the quality of medical diagnostics itself?

This Article argues that once ML diagnosticians, such as those based on neural networks, are shown to be superior, existing medical malpractice law will require superior ML-generated medical diagnostics as the standard of care in clinical settings. Further, unless implemented carefully, a physician’s duty to use ML systems in medical diagnostics could, paradoxically, undermine the very safety standard that malpractice law set out to achieve. Although at first doctor + machine may be more effective than either alone because humans and ML systems might make very different kinds of mistakes, in time, as ML systems improve, effective ML could create overwhelming legal and ethical pressure to delegate the diagnostic process to the machine. Ultimately, a similar dynamic might extend to treatment also. If we reach the point where the bulk of clinical outcomes collected in databases are ML-generated diagnoses, this may result in future decisions that are not easily audited or understood by human doctors. Given the well-documented fact that treatment strategies are often not as effective when deployed in clinical practice compared to preliminary evaluation, the lack of transparency introduced by the ML algorithms could lead to a decrease in quality of care. This Article describes salient technical aspects of this scenario particularly as it relates to diagnosis and canvasses various possible technical and legal solutions that would allow us to avoid these unintended consequences of medical malpractice law. Ultimately, we suggest there is a strong case for altering existing medical liability rules to avoid a machine-only diagnostic regime. We argue that the appropriate revision to the standard of care requires maintaining meaningful participation in the loop by physicians the loop.

Keywords: AI, Tort Law, Machine Learning, AI Policy, Medicine, Medical Malpractice Law, Health Law, Health Policy, Medical Legal Studies, Diagnosis, Neural Networks, Doctors

JEL Classification: K13

Suggested Citation

Froomkin, A. Michael and Kerr, Ian R. and Pineau, Joelle, When AIs Outperform Doctors: Confronting the Challenges of a Tort-Induced Over-Reliance on Machine Learning (February 20, 2019). 61 Ariz. L. Rev. 33 (2019), University of Miami Legal Studies Research Paper No. 18-3, Available at SSRN: https://ssrn.com/abstract=3114347 or http://dx.doi.org/10.2139/ssrn.3114347

A. Michael Froomkin (Contact Author)

University of Miami - School of Law ( email )

P.O. Box 248087
Coral Gables, FL 33146
United States
305-284-4285 (Phone)
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Yale University - Yale Information Society Project ( email )

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Ian R. Kerr

University of Ottawa - Common Law Section ( email )

57 Louis Pasteur Street
Ottawa, K1N 6N5
Canada
613-562-5800 (Phone)

Joelle Pineau

McGill University ( email )

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