Autonomous AI systems in the face of liability, regulations and costs.
Open accessAbstract
Autonomous AI systems in medicine promise improved outcomes but raise concerns about liability, regulation, and costs. With the advent of large-language models, which can understand and generate medical text, the urgency for addressing these concerns increases as they create opportunities for more sophisticated autonomous AI systems. This perspective explores the liability implications for physicians, hospitals, and creators of AI technology, as well as the evolving regulatory landscape and payment models. Physicians may be favored in malpractice cases if they follow rigorously validated AI recommendations. However, AI developers may face liability for failing to adhere to industry-standard best practices during development and implementation. The evolving regulatory landscape, led by the FDA, seeks to ensure transparency, evaluation, and real-world monitoring of AI systems, while payment models such as MPFS, NTAP, and commercial payers adapt to accommodate them. The widespread adoption of autonomous AI systems can potentially streamline workflows and allow doctors to concentrate on the human aspects of healthcare.
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Other regulated AI medical technologies, such as radiology image analysis or diagnosis, are trained on domain-specific medical data for targeted uses.
“While other regulated AI medical technologies, such as radiology image analysis177 or diagnosis,178 are trained on domain-specific medical data for targeted uses, LLMs have broader complexity, applicability, and an ability to adapt in real time, which complicates their regulation.2,179,180”
From Large Language Models for Chatbot Health Advice Studies: A Systematic Review (2025) · cites this paper as [178]
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Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638 · see citing works
Self-citationBenjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215 · see citing works
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Shrank, W. H., Rogstad, T. L., & Parekh, N. (2019) · JAMA · doi:10.1001/jama.2019.13978 · Cited by 1,071 · see citing works
Potential liability for physicians using artificial intelligence
Price, W. N., Gerke, S., & Cohen, I. G. (2019) · JAMA · doi:10.1001/jama.2019.15064 · Cited by 579 · see citing works
de Hond, A. A. H., Leeuwenberg, A. M., Hooft, L., Kant, I. M. J., Nijman, S. W. J., van Os, H. J. A., Aardoom, J. J., Debray, T. P. A., Schuit, E., van Smeden, M., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-021-00549-7 · Cited by 499 · see citing works
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Crigger, E., Reinbold, K., Hanson, C., Kao, A., Blake, K., & Irons, M. (2022) · Journal of Medical Systems · doi:10.1007/s10916-021-01790-z · Cited by 132 · see citing works
A reimbursement framework for artificial intelligence in healthcare
Abràmoff, M. D., Roehrenbeck, C., Trujillo, S., Goldstein, J., Graves, A. S., Repka, M. X., & Silva III, E. “. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00621-w · Cited by 117 · see citing works
Self-citationWhen does physician use of AI increase liability?
Tobia, K., Nielsen, A., & Stremitzer, A. (2021) · Journal of Nuclear Medicine · doi:10.2967/jnumed.120.256032 · Cited by 103 · see citing works
Morey, J., Fiano, E., Yaeger, K., et al. (2020) · medRxiv · doi:10.1101/2020.07.02.20143834 · Cited by 22 · see citing works
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Bitterman, D. S., Kamal, A., & Mak, R. H. (2023) · JAMA Oncology · doi:10.1001/jamaoncol.2023.0012 · Cited by 8 · see citing works
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Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients
Busch, F., Hoffmann, L., Xu, L., Zhang, L. J., Hu, B., García-Juárez, I., Toapanta-Yanchapaxi, L. N., Gorelik, N., Gorelik, V., Rodriguez-Granillo, G. A., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.14452 · Cited by 124 · see citing works
- Authors
- Saenz, A. D., Harned, Z., Banerjee, O., Abràmoff, M. D., & Rajpurkar, P.
- Affiliations
- Brigham and Women's Hospital (US)
- NortonLifeLock (United States) (US)
- Harvard University (US)
- University of Iowa (US)
- Corresponding author
- Rajpurkar, Pranav
- Venue
- npj Digital Medicine
- Published
- 2023-10-06
- Volume
- 6
- Issue
- 1
- Pages
- 185-185
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC10558567
- OpenAlex
- W4387405016
- Cited by
- 83
- References
- 11
- Citations by year
- 2026: 29 · 2025: 30 · 2024: 20 · 2023: 4
- FWCI
- 3.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Pharmacy › Medical Malpractice and Liability Issues
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- autonomous AI systems, artificial intelligence in healthcare, liability, malpractice, AI regulation, FDA oversight, large language models, payment models, commercial payers, physician liability, clinical workflow, health care costs
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