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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
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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 [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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In the citing paper
“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
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Generative Artificial Intelligence and Large Language Models in Clinical Oncology
Yu, Y., Zhao, Z., Wang, Z., Lin, R., Tan, Y., Chen, Y., Li, T., Baptista‐Hon, D. T., Zhang, X., Wu, C., et al. (2026) · MedComm · doi:10.1002/mco2.70833 · Cited by 2
“Patient health data are highly sensitive, and using such information for model training without explicit informed consent can lead to serious ethical controversies and a crisis of public trust [ 205 ].”
Chang, J., Peng, R., Chen, X., Zhu, Y., Miao, R., Cao, Z., & Feng, H. (2026) · Journal of Medical Internet Research · doi:10.2196/84726 · Cited by 3
Umer, F., Mr, S., & Rahman, A. J. (2026) · BDJ Open · doi:10.1038/s41405-026-00462-9 · Cited by 2
“A comprehensive adversarial evaluation framework typically incorporates three complementary approaches: Together, these three complementary approaches form the foundation of robust adversarial evaluation in healthcare AI [10, 14, 22, 25] (Fig.”
Seifaddini, M., Beheshti, M., Richberg, S., Esebua, M., & Zachary, I. (2026) · Modern Pathology · doi:10.1016/j.modpat.2026.101043 · Cited by 2
Ethics in AI: Integrating Ancient Indian Wisdom into Large Language Models
Triapthi, Y., Saunkhla, A. S., & Sharma, A. (2025) · International Conference on Signal Processing, Computing and Control (ISPCC) · doi:10.1109/ispcc66872.2025.11039362 · Cited by 2
Tran, H. H.-V., Thu, A., Twayana, A. R., Fuertes, A., Gonzalez, M., Basta, M., James, M., Frishman, W. H., & Aronow, W. S. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000001042 · Cited by 2
Modi, M., Krull, J. E., Gauntner, T. D., Li, M., Johnson, D., Wang, X., Cheng, H., Zhao, J., Ma, Q., Zhang, P., et al. (2026) · npj Digital Medicine · doi:10.64898/2026.01.26.26344845 · Cited by 3
Medical students' use of large language models: a national survey
Barr, A. A., Rozman, R. C., Liu, K., Pham, M., Klarenbach, Z., Chinna-Meyyappan, A., Hassan, A. Y., Zarychta, M., Ferri, O. E., Al-Khaz’Aly, A., et al. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106589 · Cited by 1
Using large language models to generate child-friendly education materials on myopia
Li, X., Zhang, Y., Zheng, T., Deng, Y., Lu, Y., Hu, J., Chen, S., Li, Y., & Wang, K. (2025) · Digital Health · doi:10.1177/20552076251362338 · Cited by 1
“Prior studies have demonstrated promising applications of LLMs in ophthalmology 7,8 and broader medical domains 9 – 13 but have also revealed variable accuracy and limitations in clinical reasoning.”
Using KeyGraph and ChatGPT to Detect and Track Topics Related to AI Ethics in Media Outlets
Li, W.-H., & Yu, H. (2025) · Mathematics · doi:10.3390/math13172698 · Cited by 1
Hirani, H., Saboo, B., Modi, A., Modi, P. R., Samajdar, S. S., Saboo, B., Chawla, M. S., Maheshwari, A., Gupta, A. S., Parikh, R. M., et al. (2025) · International Journal of Diabetes and Technology · doi:10.4103/ijdt.ijdt_31_25 · Cited by 1
Macharia, P. M., Hartley, A., Ding, J., Kan, S., Currie, S., Wamalwa, P., Ngugi, L., Nyongesa, T., Mukhwana, R., Mbwayo, A., et al. (2025) · Open Research Africa · doi:10.12688/openresafrica.16217.1 · Cited by 1
Dietrich, N. T., Bradbury, N., & Loh, C. (2025) · American Journal of Roentgenology · doi:10.2214/ajr.25.33947 · Cited by 1
Using generative AI for the objective assessment of language in healthcare
O’Sullivan, J., Garcés, P., Aponte, E. A., Tillmann, J., Chatham, C. H., Lipsmeier, F., & Nobbs, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-26944-8 · Cited by 1
Li, Z., & Zhu, C. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1697053 · Cited by 2
“In high-stakes clinical deployments, recent discussions around large language models reinforce the regulatory premium on traceable, proportionate interventions (Ong et al., 2024).”
Artificial Intelligence-Enabled Devices in Neurology: Mapping the Present and Future
Amurthur, A., McCarthy, D. J., Schwamm, L. H., & Hillis, J. M. (2025) · Seminars in Neurology · doi:10.1055/a-2772-7189 · Cited by 1
Karaman, A. K., Yılmaz, B., Acar, K. H., Alizada, S., Korkmazer, B., Kartum, T. A., Samancı, C., Dağdelen, M., Çomunoğlu, N., Arslan, S. A., et al. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01793-z · Cited by 1
Lin, T., Wan, H., Liang, Y., Ming, J., Lu, J., & Guo, Z. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1679496 · Cited by 1
“While LLMs effectively assist in predicting SCAP mortality, challenges such as data privacy, model generalization, and potential biases need further exploration to validate their applicability across different datasets and populations (39, 46 – 48).”
Gökmen, M. Y., Belibağlı, M. C., Karıncaoğlu, E., NAZLICAN, M. Ç., Türkmen, E., Yönal, M., & Uluöz, E. (2026) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-026-01559-2 · Cited by 2
“Large language models (LLMs), which generate human-like reasoning from textual input, have been proposed as tools to bridge the divide between guideline text and practical implementation [3, 4].”
Gravina, A. G., Pellegrino, R., Imperio, G., Izzo, M., Costanzo, I. D., Landa, F., Pellegrino, S., & Federico, A. (2026) · Intestinal Research · doi:10.5217/ir.2025.00138 · Cited by 1
“11,12 Thanks to VTP, the LLM can process images in addition to text.”
Gotlib, J., Włodarczyk, K., Koczkodaj, P., Hreńczuk, M. K., Nowakowski, A., Pańczyk, M., & Cieślak, I. (2026) · Health Problems of Civilization · doi:10.29316/hpc/218352 · Cited by 1
Wang, Y., Zhang, C., Zhao, H., Wang, C., Guo, L., Wei, P., Jin, M., Li, A., Li, Q., & Pan, H. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2026.1693381 · Cited by 1
Automated auditing of emergency department documentation using large language models
Dietrich, N., & Stubbert, B. (2026) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2026.03.007 · Cited by 1
Torres-Costa, S., Machado, S., Barbosa, G., Carvalho, E., Guerra, A., Ramião, N., Penas, S. C., Parente, M., & Falcão, M. S. (2026) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2026.03.016 · Cited by 1
Ren, K., Weng, Q., Chen, Q., Li, H., Xie, D., Zeng, C., Wei, J., Lei, G., & Wang, Y. (2026) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-026-06844-x · Cited by 1
“On the other hand, LLMs can impede the development of critical thinking, and even cause a series of ethical and legal issues, which necessitates stringent oversight in the future application [26, 27].”
Advances in generative biology and bio-inspired artificial intelligence
Wen-Ye, Z. E. N. G., ZHENG, S.-Y., Xiao-Qin, Z. H. A. O., Zhu, W. A. N. G., ZHANG, Y.-J., & Zheng-Yuan, M. A. (2026) · Shengming kexue · doi:10.3724/cbls.2026038 · Cited by 1
Harnessing artificial intelligence for pediatric health: Current trends and future opportunities
Wu, J., Chen, B., Chan, K. C. C., Liang, M., Lyu, Y., Yu, P., Feng, T., Xie, F., Yu, S., Zhang, F., et al. (2026) · iScience · doi:10.1016/j.isci.2026.115962 · Cited by 1
Aroua, R., Kammoun, I., Louati, M. A., Adjeroh, D., & Duan, T. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1816692 · Cited by 1
“…the lack of standardized PHI-aware benchmarks, limited transparency around “de-identification assessment” (Zhong et al., 2025), and the need for human-centered evaluation that captures clinician judgment and patient privacy expectations when models are used in real care settings (Ong et al., 2024).”
Harbrücker, M., Menge, F., Taebi, A., Nörenberg, D., Speer, T., Reißfelder, C., Hohenberger, P., Jakob, J., Li, C., & Yang, C. (2026) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-026-06489-7 · Cited by 1
“Ethical and regulatory concerns—including transparency, accountability, and equity of access—must be addressed before deployment at scale (Ong et al. 2024).”
Ye, S., Jin, Z., Li, W. W. L., Wei, G., Jiang, S., & Wang, Q. Y. (2026) · Breast Cancer Research · doi:10.1186/s13058-026-02303-x · Cited by 1
“Simultaneously, reinforcing privacy safeguards through encryption and differential privacy techniques remains critical to safeguarding sensitive health information [118].”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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