Payments to Health Care Professionals and Teaching Hospitals by AI- and Machine Learning-Enabled Medical Device Manufacturers

David-Dan Nguyen^{1

2

3}, Anju Murayama^{4

5

6}, Tiange Li³, Joshua S Cheruvathur³, Lina M Freeman⁷, Christopher J D Wallis^{3

8}, Laura C Rosella^{1

2

9}, Girish S Kulkarni^{1

3}, Genevieve P Kanter^{10

11}, Deborah C Marshall^{5

12}, Joseph S Ross^{13

14}, Allan S Detsky^{1

15

16}

Affiliations

¹ Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
² Schwartz Reisman Institute for Technology and Society, University of Toronto, Toronto, Ontario, Canada.
³ Division of Urology and Surgical Oncology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
⁴ School of Medicine, Tohoku University, Sendai, Miyagi, Japan.
⁵ Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York City, New York.
⁶ Clinical Training Center, Obihiro Kosei Hospital, Obihiro, Hokkaido, Japan.
⁷ Clinical Training Center, Ashikaga Red Cross Hospital, Ashikaga, Tochigi, Japan.
⁸ Division of Urology, Department of Surgery, Mount Sinai Hospital, University of Toronto, Ontario, Canada.
⁹ Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
¹⁰ Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles.
¹¹ Department of Health Policy and Management, Sol Price School of Public Policy, University of Southern California, Los Angeles.
¹² Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York City, New York.
¹³ Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut.
¹⁴ Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
¹⁵ Department of Medicine, Mount Sinai Hospital & University Health Network, Toronto, Ontario, Canada.
¹⁶ Department of Medicine, University of Toronto, Toronto, Ontario, Canada.

PMID: 40982240
PMCID: PMC12455483 (available on 2026-03-22)
DOI: 10.1001/jama.2025.14813

Payments to Health Care Professionals and Teaching Hospitals by AI- and Machine Learning-Enabled Medical Device Manufacturers

David-Dan Nguyen et al. JAMA. 2025.

. 2025 Sep 22:e2514813.

doi: 10.1001/jama.2025.14813. Online ahead of print.

Authors

Affiliations

¹ Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.
² Schwartz Reisman Institute for Technology and Society, University of Toronto, Toronto, Ontario, Canada.
³ Division of Urology and Surgical Oncology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
⁴ School of Medicine, Tohoku University, Sendai, Miyagi, Japan.
⁵ Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York City, New York.
⁶ Clinical Training Center, Obihiro Kosei Hospital, Obihiro, Hokkaido, Japan.
⁷ Clinical Training Center, Ashikaga Red Cross Hospital, Ashikaga, Tochigi, Japan.
⁸ Division of Urology, Department of Surgery, Mount Sinai Hospital, University of Toronto, Ontario, Canada.
⁹ Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
¹⁰ Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles.
¹¹ Department of Health Policy and Management, Sol Price School of Public Policy, University of Southern California, Los Angeles.
¹² Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York City, New York.
¹³ Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut.
¹⁴ Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
¹⁵ Department of Medicine, Mount Sinai Hospital & University Health Network, Toronto, Ontario, Canada.
¹⁶ Department of Medicine, University of Toronto, Toronto, Ontario, Canada.

PMID: 40982240
PMCID: PMC12455483 (available on 2026-03-22)
DOI: 10.1001/jama.2025.14813

No abstract available

Plain language summary

This study examines the extent and nature of payments made to health care professionals and teaching hospitals by manufacturers of FDA-authorized AI- and machine learning–enabled devices.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Nguyen reported receiving grants from Movember Foundation outside the submitted work and being supported by a Canadian Institutes of Health Research (CIHR) Vanier Canada Graduate Scholarship (CGV-192647), the CMCC/Atrium Hold’em for Life Oncology Fellowship at the University of Toronto, and the Schwartz Reisman Institute for Technology and Society Fellowship at the University of Toronto. Dr Wallis reported being supported by the Hold’em for Life Early Career Professor in Cancer Research, a University limited-term named professorship at the University of Toronto, and reported receiving personal fees from Janssen Oncology, AstraZeneca, Astellas, Intuitive Surgical, Pfizer, TerSera, Nanostics, Sumitomo, Merck, and AbbVie and grants from Tolmar, Bayer, and Knight Pharmaceuticals outside the submitted work. Dr Kulkarni reported receiving grants from Johnson & Johnson and personal fees from Johnson & Johnson, Merck, BMS, Ferring, Protara, EMD Serono, AbbVie, Tolmar, Knight Pharmaceuticals, Theralase, and Astellas outside the submitted work. Dr Ross reported receiving grants from the US Food and Drug Administration, Johnson & Johnson, Medical Devices Innovation Consortium, Agency for Healthcare Research and Quality, National Institutes of Health/National Heart, Lung, and Blood Institute, and Arnold Ventures outside the submitted work and serving as an expert witness at the request of relator’s attorneys, Greene Law Firm, in a qui tam suit alleging violations of the False Claims Act and Anti-Kickback Statute against Biogen Inc that settled in September 2022. No other disclosures were reported.

References

1. Warraich HJ, Tazbaz T, Califf RM. FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine. JAMA. 2025;333(3):241-247. doi: 10.1001/jama.2024.21451 - DOI - PubMed
1. Windecker D, Baj G, Shiri I, et al. Generalizability of FDA-Approved AI-Enabled Medical Devices for Clinical Use. JAMA Netw Open. 2025;8(4):e258052-e258052. doi: 10.1001/jamanetworkopen.2025.8052 - DOI - PMC - PubMed
1. Smith JA, Abhari RE, Hussain Z, Heneghan C, Collins GS, Carr AJ. Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study. BMJ Open. 2020;10(10):e039969. doi: 10.1136/bmjopen-2020-039969 - DOI - PMC - PubMed
1. Muehlematter UJ, Bluethgen C, Vokinger KN. FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks. Lancet Digit Health. 2023;5(9):e618-e626. doi: 10.1016/S2589-7500(23)00126-7 - DOI - PubMed
1. Shah NH, Halamka JD, Saria S, et al. A Nationwide Network of Health AI Assurance Laboratories. JAMA. 2024;331(3):245-249. doi: 10.1001/jama.2023.26930 - DOI - PubMed

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Payments to Health Care Professionals and Teaching Hospitals by AI- and Machine Learning-Enabled Medical Device Manufacturers

Affiliations

Payments to Health Care Professionals and Teaching Hospitals by AI- and Machine Learning-Enabled Medical Device Manufacturers

Authors

Affiliations

Plain language summary

Conflict of interest statement

References

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