Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine

Thomas J. Hwang; Aaron S. Kesselheim; Kerstin N. Vokinger

doi:10.1001/jama.2019.16842

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November 22, 2019

Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine

Thomas J. Hwang, AB^1,2; Aaron S. Kesselheim, MD, JD, MPH³; Kerstin N. Vokinger, MD, JD, PhD^3,4

Author Affiliations Article Information

¹Harvard Medical School, Boston, Massachusetts
²Laboratory for Technology, Markets, and Regulation, University of Zurich, Zurich, Switzerland
³Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
⁴Institute of Law and Laboratory for Technology, Markets, and Regulation, University of Zurich, Zurich, Switzerland

JAMA. 2019;322(23):2285-2286. doi:10.1001/jama.2019.16842

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2 Comments for this article

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November 23, 2019

Software Lifecycle Regulation

Hugh Harvey, MBBS BSc FRCR MD | Hardian Health

The authors may wish to read up on the international standards that already exist in regards to medical device software regulation, in particular the IEC standard 62304 which specifically addresses multiple points within their article pertaining to lifecycles of software (1).

In general, there is an ongoing trend for academics (who have never been involved in producing commercial software) penning such articles claiming that regulation either doesn’t exist or needs improving. This is due to a lack of awareness, as the academic remit does not usually extend into commercialisations and the jury-gritty of medics device software development.

Software developers in industry have, for decades, been working to ISO and IEC standards, and there is an entire thriving business of quality assurance and regulatory affairs which covers these topics in immense detail.

REFERENCE

1. https://www.iso.org/standard/38421.html

CONFLICT OF INTEREST: None Reported

December 9, 2019

Response to Comment "Software Lifecycle Regulation"

Kerstin Vokinger, MD, JD, PhD | University of Zurich, Switzerland / Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School

Voluntary frameworks and industry standards, including the one referenced by the commenter as well as those by other bodies, can be very useful but are not a substitute for FDA's decisions. As the FDA states in guidance to industry, the use of consensus standards "generally satisfies only a portion of a premarket submission." (1)

The key reforms discussed in this article would require new FDA action and/or legislative action (e.g., time-limited approvals). This is consistent with the FDA's view that additional statutory authority is likely required to optimize premarket and postmarket review of AI/ML medical products. (2)

We encourage all interested stakeholders to participate in FDA's consultation on its regulatory approach to these products. (2)

References:

(1) FDA Guidance. https://www.fda.gov/media/71983/download
(2) Regulations.gov Docket. https://www.regulations.gov/docket?D=FDA-2019-N-1185

CONFLICT OF INTEREST: None Reported

Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine

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