The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Reclaiming informed consent to train mental health AI with patient data
Open accessAbstract
Companies increasingly train AI models on therapy transcripts, often relying on terms of service that obscure data use to obtain assent. Because AI systems generate inferences from data and enable unforeseen secondary uses, existing consent models do not adequately protect autonomy and confidentiality. We argue for (1) separate and explicit opt-in consent to train on patient data and (2) patient-led governance to prevent the recurrence of exploitation in medical innovation.
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Cites
Privacy in the age of medical big data
Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522
Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00003-6 · Cited by 266
Lessons from HeLa Cells: The Ethics and Policy of Biospecimens
Beskow, L. M. (2016) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083115-022536 · Cited by 146
Flagged first, then by citations
Research works printed but not linked: 22
- Fisher, M. A. Laws affecting confidentiality. In The Ethics of Conditional Confidentiality: A Practice Model for Mental Health Professionals (Oxford Univ. Press, 2013). · DOI
- Chevrier, R., Foufi, V., Gaudet-Blavignac, C., Robert, A. & Lovis, C. Use and understanding of anonymization and de-identification in the biomedical literature: scoping review. J. Med. Internet Res. 21, e13484 (2019). · DOI
- Becker, R. et al. Purpose definition as a crucial step for determining the legal basis under the GDPR: implications for scientific research. J. Law Biosci. 11, lsae001 (2024). · DOI
- Su, W. et al. Assessing the readability of app descriptions and investigating its role in the choice of mHealth apps: retrospective and prospective analyses. AMIA Annu. Symp. Proc. 2021, 1139–1148 (2022).
- Froomkin, A. M. Big data: destroyer of informed consent. Yale J. Law Technol 21, 27–52 (2019).
- Pham, T. Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use. R. Soc. Open Sci. 12, 241873 (2025). · DOI
- Moulaei, K., Akhlaghpour, S. & Fatehi, F. Patient consent for the secondary use of health data in artificial intelligence (AI) models: A scoping review. Int. J. Med. Inform. 198, 105872 (2025). · DOI
- Wachter, S. & Mittelstadt, B. A right to reasonable inferences: rethinking data protection law in the age of big data and AI. Colum. Bus. Law Rev. 2019, 494–620 (2019).
- Hua, Y. et al. A scoping review of large language models for generative tasks in mental health care. npj Digit. Med 8, 230 (2025). · DOI · Paper page
- Rezaeikhonakdar, D. AI chatbots and challenges of HIPAA compliance for AI developers and vendors. J. Law Med. Ethics 51, 988–995 (2023). · DOI · Paper page
- Pendse, S. R. et al. Advancing a consent-forward paradigm for digital mental health data. Nat. Mental Health 2, 1298–1307 (2024). · DOI
- Cervera de la Cruz, P., Lalova-Spinks, T. & Shabani, M. The European Health Data Space: an opportunity to strengthen citizen rights and engage citizens in health data governance. Front. Med. 12, 1699941 (2025). · DOI
- Custers, B. & Vrabec, H. Tell me something new: data subject rights applied to inferred data and profiles. Comput. Law Secur. Rev. 52, 105956 (2024). · DOI
- Sweeney, L. et al. Re-identification risks in HIPAA Safe Harbor Data: a study of data from one environmental health study. Technol. Sci. 2017, 2017082801 (2017).
- Carriere-Swallow, Y. & Haksar, V. The economics and implications of data: an integrated perspective. IMF Dept. Pap 2019, 013 (2019).
- Williams, M., Karim, W., Gelman, J. & Raza, M. Ethical data acquisition for LLMs and AI algorithms in healthcare. npj Digit. Med. 7, 377 (2024). · DOI
- Muller, S. H., van Thiel, G. J., Mostert, M. & van Delden, J. J. Dynamic consent, communication and return of results in large-scale health data reuse: Survey of public preferences. Digit. Health 9, 1–14 (2023).
- Ploug, T. & Holm, S. Meta consent: a flexible and autonomous way of obtaining informed consent for secondary research. BMJ 350, h2146 (2015). · DOI
- DeCamp, M. et al. Patients’ views about patient engagement and representation in healthcare governance. J. Healthc. Manag 64, 332–346 (2019).
- Azad, T. D., Warman, A., McGraw, D. & Saria, S. Lessons from Henrietta Lacks inform a transparency framework to catalyze generative artificial intelligence in medicine. npj Digit. Med 8, 280 (2025). · DOI
- Quyoum, A. & Wong, M. Valuing lived experience and co-design solutions to counter racial inequality in data and algorithmic systems in UK’s digital services. Inf. Commun. Soc. 27, 1793–1809 (2024). · DOI
- Shaw, J. & Sekalala, S. Health data justice: Building new norms for health data governance. npj Digit. Med 6, 30 (2023). · DOI
Metadata
- Authors
- Larrauri, C. A., Martinez-Martin, N., & Torous, J.
- Affiliations
- Harvard University (US)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Larrauri, Carlos A.
- Venue
- npj Digital Medicine
- Published
- 2026-06-02
- Volume
- 9
- Issue
- 1
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC13230920
- OpenAlex
- W7163176454
- Cited by
- 2
- References
- 3
- Citations by year
- 2026: 2
- FWCI
- 12.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Michigan
- College of Pharmacy, University of Michigan
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- informed consent, patient information, opt-in consent, data governance, patient autonomy, confidentiality, data reuse, AI training data
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