I wrote this piece in collaboration with my long-time pal and We Robot co-founder Michael Froomkin and our genius colleague in machine learning, Joëlle Pineau. In it, we observe that someday, perhaps soon, diagnostics generated by machine learning (ML) will have demonstrably better success rates than those generated by human doctors. In that context, we ask what the dominance of ML diagnostics will 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?

In our view, once ML diagnosticians 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 as well. 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 no longer easily audited or even 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 the overall quality of care. My co-authors and I describe 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 conclude that the appropriate revision to the standard of care requires maintaining meaningful participation by physicians the loop.

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