Evidence map›Paper›PMID 41357259›Full record

ArticleHealth affairs scholar2025

Characterizing industry payments for FDA-approved AI medical devices.

Alon Bergman, Tej A Patel, Kaustav P Shah

Abstract read
In one paragraph

Article in Health affairs scholar, 2025. 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

3 authors.

Alon BergmanDepartment of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA 19104, United States.ORCID https://orcid.org/0000-0003-4228-8543
Tej A PatelThe Wharton School, University of Pennsylvania, Philadelphia, PA 19104, United States.ORCID https://orcid.org/0000-0002-0882-2960
Kaustav P ShahLeonard Davis Institute of Health Economics, Philadelphia, PA 19104, United States.ORCID https://orcid.org/0000-0001-6866-0003

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence-enabled medical devices (AIMDs) are increasing in use, but this growth has raised concerns about inequities in access across well-resourced and under-resourced settings. Little is known about industry-clinician partnerships in the AIMD ecosystem. Methods: We examined the value, specialty distribution, market concentration, and institutional profile of payments made by industry to clinicians for Food and Drug Administration-approved AIMDs using the Open Payments Database. We linked payments to the affiliated hospital of the clinician using the Medicare Provider Data catalog. We performed a regression to explain the association of payments with hospital and county-level factors. Results: We found $59.3 million was spent on payments to 46 315 clinicians for AIMDs between 2017 and 2023, representing an increasing share of total medical device payments over time. We saw high payment concentration in technologically intensive medical specialties and among clinicians affiliated with large, urban teaching hospitals. Conclusion: Industry payments for AIMDs are increasing and concentrated among technology-intensive specialties. Payments are more likely to flow to clinicians affiliated with teaching hospitals that are larger and in non-rural areas. This may reflect or mediate increased AI utilization in these settings. Continued monitoring of payments, transparent reporting, and targeted resource support may be needed to promote equitable access to AIMDs.

Indexed as

artificial intelligenceconflictsdigital dividemedical devicesopen paymentsphysicians

Identifiers

PMID41357259
PMCPMC12679402

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.