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Problematic Interactions Between AI and Health Privacy
Open accessAbstract
The interaction of artificial intelligence (AI) and health privacy is a two-way street. Both directions are problematic. This Essay makes two main points. First, the advent of artificial intelligence weakens the legal protections for health privacy by rendering deidentification less reliable and by inferring health information from unprotected data sources. Second, the legal rules that protect health privacy nonetheless detrimentally impact the development of AI used in the health system by introducing multiple sources of bias: collection and sharing of data by a small set of entities, the process of data collection while following privacy rules, and the use of non-health data to infer health information. The result is an unfortunate anti-synergy: privacy protections are weak and illusory, but rules meant to protect privacy hinder other socially valuable goals. The state of affairs creates biases in health AI, privileges commercial research over academic research, and is ill-suited to either improve health care or protect patients. The health system deeply needs a new bargain between patients and the health system about the uses of patient data.
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“Specifically, in the health care domain, data often exhibit bias due to the uneven distribution of demographic attributes, preconceptions held by health care professionals involved in data collection and interpretation, and the varied academic and experiential backgrounds that influence their perspectives [175-177].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [176]
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Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349
What Should ChatGPT Mean for Bioethics?
Cohen, I. G. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2233357 · Cited by 99
Kamitani, E., Koenig, L. J., & Sullivan, P. S. (2025) · AIDS · doi:10.1097/qad.0000000000004220 · Cited by 4
Medical Data Privacy and Security in the AI Age
Hoffman, S. (2025) · The International Library of Bioethics · doi:10.1007/978-3-031-94690-5_14 · Cited by 1
Solaiman, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4886518 · Cited by 1
Practical Steps in Implementing Privacy Measures With Synthetic Health Data
Pierce, D. V., Li, Y., Greenshaw, A. J., Bailey, T. M., & Cao, B. (2025) · World Medical & Health Policy · doi:10.1002/wmh3.70023
Building Trust in Government AI: Balancing Innovation and Privacy in Healthcare Services
Robles, P., Azevedo, L., Best, E., & Mallinson, D. J. (2026) · Public Organization Review · doi:10.1007/s11115-026-01018-z · Cited by 1
Metadata
- Authors
- Price, N.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Nicholson Price
- Venue
- Utah Law Review Society
- Publisher
- Utah Law Review Society
- Published
- 2021
- OpenAlex
- W6908377304
- Cited by
- 8
- References
- 0
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 2
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Locations
- eYLS (Yale Law School) (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- J. Willard Marriott Library
- Topics
- Health Sciences › Health Professions › Health Information Management › Thoreau and American Literature
- Social Sciences › Law › Digitalization, Law, and Regulation
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, patient privacy, de-identification, algorithmic bias, privacy law, health data sharing, commercial research
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