Machine Vision, Medical AI, and Malpractice

Zach Harned, Matthew P. Lungren & Pranav Rajpurkar, Comment, Machine Vision, Medical AI, and Malpractice, Harv. J.L. & Tech. Dig. (2019)

10 Pages Posted: 28 Aug 2019

See all articles by Zach Harned

Zach Harned

Stanford Law School

Matthew P. Lungren

Stanford University, School of Medicine, Center for Population Health Sciences, Department of Biomedical Data Science; Stanford University - Center for Artificial Intelligence in Medicine and Imaging

Pranav Rajpurkar

Stanford University - Department of Computer Science

Date Written: January 21, 2019

Abstract

The introduction of novel medical technology into clinical practice gives rise to novel questions of legal liability when something goes wrong. The complexity of the technology is often paralleled by the complexity of the liability analysis, which is why questions of malpractice involving medical artificial intelligence are so vexing. There are myriad medical use cases for artificial intelligence (AI), but some of the most promising applications involve the use of machine vision for imaging diagnostics.

However, these machine vision applications involve complicated software models, the operation of which can be opaque at times even to its designers. This introduces concerns from physicians over whether they can trust a machine they do not fully understand or rely on its judgements. This can also arouse fear over the possibility of malpractice claims.

Some of the recent advances in machine learning technology make its results easier to interpret, allowing medical professionals to feel more confident in using the technology. This article illustrates how such innovations are likely to impact the legal system and malpractice suits. We conclude that the unique capabilities and functions of AI and machine vision, especially when conjoined with the aforementioned advances in their interpretability, create an opportunity to argue that the technology actually minimizes physician liability.

These advances in machine vision interpretability also change the legal landscape for the manufacturers of this technology. We examine impacts to products liability, focusing specifically on the issue of whether such technology would (or will soon) be considered a "product," and how this might affect manufacturers’ product development and marketing strategies. We also consider how the learned intermediary defense might be deployed in failure-to-warn cases involving medical machine vision, again looking to how the legal doctrine is likely to impact manufacturer behavior in the design and deployment of such technologies.

Keywords: machine learning, artificial intelligence, malpractice, liability, products liability, machine vision, medical, medicine, legal, law

JEL Classification: I1, K13, K32, O33, C80

Suggested Citation

Harned, Zach and Lungren, Matthew P. and Rajpurkar, Pranav, Machine Vision, Medical AI, and Malpractice (January 21, 2019). Zach Harned, Matthew P. Lungren & Pranav Rajpurkar, Comment, Machine Vision, Medical AI, and Malpractice, Harv. J.L. & Tech. Dig. (2019), Available at SSRN: https://ssrn.com/abstract=3442249

Zach Harned (Contact Author)

Stanford Law School ( email )

559 Nathan Abbott Way
Stanford, CA 94305
United States

Matthew P. Lungren

Stanford University, School of Medicine, Center for Population Health Sciences, Department of Biomedical Data Science ( email )

291 Campus Drive
Li Ka Shing Building
Stanford, CA
United States

Stanford University - Center for Artificial Intelligence in Medicine and Imaging ( email )

CA
United States

Pranav Rajpurkar

Stanford University - Department of Computer Science

Gates Computer Science Building
353 Serra Mall
Stanford, CA 94305-9025
United States

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