The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Ethical and regulatory challenges of large language models in medicine
Open accessAbstract
With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs. A comprehensive framework and mitigating strategies will be imperative for the responsible integration of LLMs into medical practice, ensuring alignment with ethical principles and safeguarding against potential societal risks.
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“Second, it may erode academic credibility as the boundary between authentic scholarship and machine-generated output becomes increasingly blurred (Ong et al., 2024).”
From The Attention Mismatch: Mapping the Structural Academic Governance Deficit in the Age of Generative AI (2026) · cites this paper as Ong et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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 [175]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“If M-LLMs are trained on patient data that contain biases related to gender, ethnicity, socioeconomic status, or geographic location, they may inadvertently cause biases in their predictions or recommendations [175,178,179].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [175]
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“Furthermore, data pre-processing decisions vary drastically across research teams, which can lead to integrating sensitive or protected health information (PHI) into LLMs, creating security, privacy, and regulatory risks.3”
From Development of secure infrastructure for advancing generative artificial intelligence research in healthcare at an academic medical center (2025) · cites this paper as [3]
Experimental — accuracy not yet measuredIn the citing paper
“There is a potential for misuse of patient data, medicolegal implications, and loss of physician-patient trust.181,192”
From Large Language Models for Chatbot Health Advice Studies: A Systematic Review (2025) · cites this paper as [181]
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Cites
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944
Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028
The imperative for regulatory oversight of large language models (or generative AI) in healthcare
Meskó, B., & Topol, E. J. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00873-0 · Cited by 1,047
A governance model for the application of AI in health care
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz192 · Cited by 979
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.
Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1041-y · Cited by 665
Feng, J., Phillips, R. V., Malenica, I., Bishara, A., Hubbard, A. E., Celi, L. A., & Pirracchio, R. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00611-y · Cited by 423
Collingridge and the dilemma of control: Towards responsible and accountable innovation
Genus, A., & Stirling, A. (2018) · Research Policy · doi:10.1016/j.respol.2017.09.012 · Cited by 301
Auditing Large Language Models: A Three-Layered Approach
Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2023) · doi:10.2139/ssrn.4361607 · Cited by 292
Auditing large language models: a three-layered approach
Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2024) · AI and Ethics · doi:10.1007/s43681-023-00289-2 · Cited by 289
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
Cited by
Sorted by citations
Shaping the future of healthcare: ethical clinical challenges and pathways to trustworthy AI
Goktas, P., & Grzybowski, A. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14051605 · Cited by 336
“Transparency : The AI model’s decision-making process is explainable through heatmaps and confidence scores, helping clinicians validate its outputs.”
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
“If M-LLMs are trained on patient data that contain biases related to gender, ethnicity, socioeconomic status, or geographic location, they may inadvertently cause biases in their predictions or recommendations [175,178,179].”
Large Language Models for Chatbot Health Advice Studies: A Systematic Review
Huo, B., Boyle, A., Marfo, N., Tangamornsuksan, W., Steen, J. P., McKechnie, T., Lee, Y., Mayol, J., Antoniou, S. A., Thirunavukarasu, A. J., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.57879 · Cited by 283
The Opportunities and Risks of Large Language Models in Mental Health
Lawrence, H. R., Schneider, R. A., Rubin, S. B., Matarić, M. J., McDuff, D. J., & Jones Bell, M. (2024) · JMIR Mental Health · doi:10.2196/59479 · Cited by 235
Freyer, O., Wiest, I. C., Kather, J. N., & Gilbert, S. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00124-9 · Cited by 172
Metadata
- Authors
- Ong, J. C. L., Chang, S. Y.-H., William, W., Butte, A. J., Shah, N. H., Chew, L. S. T., Liu, N., Doshi-Velez, F., Lu, W., Savulescu, J., & Ting, D. S. W.
- Affiliations
- National University of Singapore (SG)
- University of California, San Francisco (US)
- Stanford Health Care (US)
- SingHealth (SG)
- Corresponding author
- Ting, Daniel Shu Wei
- ORCID
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Issue
- 6
- Pages
- e428-e432
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W4395026179
- Cited by
- 342
- References
- 23
- Citations by year
- 2026: 165 · 2025: 136 · 2024: 41
- FWCI
- 31.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- A*STAR
- National University of Singapore (NUHSRO/2022/078/Startup/13, NUS Start-Up)
- Duke-NUS Medical School
- Wellcome Trust (WT203132/Z/16/Z)
- National Medical Research Council
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- large language models, medical ethics, AI regulation, data privacy, data provenance, intellectual property, user rights, responsible AI, medical practice, societal risks of AI
- MeSH terms
- Artificial Intelligence, Humans, Natural Language Processing, Intellectual Property
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