Evidence map›Paper›PMID 41815749›Full record

ArticleHealth affairs scholar2026

The history of state preemption and medical device regulation: lessons for artificial intelligence oversight.

Case Thomason, Larry Bucshon, Brian J Miller, Brian Yagi

Abstract read
In one paragraph

Article in Health affairs scholar, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Case ThomasonUniversity of Michigan Law School, Ann Arbor, MI 48109, United States.
Larry BucshonHolland & Knight L.L.P., Washington, DC 20006, United States.
Brian J MillerDivision of Hospital Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States.ORCID https://orcid.org/0000-0003-3247-8845
Brian YagiDivision of Hospital Medicine, Department of Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid expansion of artificial intelligence (AI) in health care has outpaced the United States' fragmented regulatory structure, which currently relies on overlapping albeit incomplete federal oversight from multiple agencies. In the absence of clear federal product standards, some states have advanced broad and inconsistent regulatory schemes that often sweep AI-enabled medical devices into consumer-protection frameworks not designed for clinically used technologies. These state-level requirements risk recreating the fragmented medical device regulatory landscape that existed prior to 1976, where inconsistent rules increased compliance burdens and undermined patient safety. Congress responded with the 1976 Medical Device Amendments (MDA), granting FDA jurisdiction over medical devices and simultaneously adding a preemption provision prohibiting states from enacting or enforcing requirements "different from, or in addition to" federal requirements while still respecting states' rights to regulate use in medical practice. Drawing on historical parallels to the MDA and emerging state laws, this article proposes a preemption clause limited to health care-related AI that is coupled with a modernized, flexible federal product oversight framework-grounded in the FDA's technical expertise and informed by least-burdensome principles. Patients, physicians, and entrepreneurs would gain regulatory clarity, reduced compliance fragmentation, and a supportive regulatory environment for pragmatic, responsible technological advancement.

Indexed as

artificial intelligence regulationfederalismFood and Drug Administrationmedical device regulationpreemption

Identifiers

PMID41815749
PMCPMC12975178

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.