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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]
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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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Citations
Cites
Large language models in medicine
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“The ethical and legal implications of using LLM models in dermatology also require further investigation [70].”
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Yıldız, A. Y., & Kalayci, A. (2025) · IEEE International Conference on AI and Data Analytics (ICAD) · doi:10.1109/icad65464.2025.11114069 · Cited by 57
“These concerns and challenges are broadly justified in the literature in terms of its acceptability [34], downstream accuracy [35], medical ethics [36], [37].”
Understanding natural language: Potential application of large language models to ophthalmology
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The potential of large language models to advance precision oncology
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“73 Hence, establishing agencies that possess supervision authority for legally acquiring, utilizing, and storing data, as well as approving medical devices, is necessary before clinically applying any LLM on the market.”
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“10 The third principle pertains to the amount and substantiality of original material used to train the generative AI model, 10 and clarity regarding the origin of training data and permission or license to use content or data protected by copyright is recommended.”
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“This is concerning since in healthcare, there is already a signi fi cant apprehension related to biases in AI algorithms, privacy, harm to patients with the use of toxic language, and safety concerns with misinformation generated from hallu-cinating LLM output or targeted attacks 9,15 .”
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“49 The current work has some limitations.”
Simulated misuse of large language models and clinical credit systems
Anibal, J., Huth, H. B., Gunkel, J., Gregurick, S. K., & Wood, B. J. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01306-2 · Cited by 13
Freyer, O., Wiest, I. C., & Gilbert, S. (2025) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2025.100196 · Cited by 12
Singh, R., Hamouda, M., Chamberlin, J., Tóth, A., Munford, J., Silbergleit, M., Baruah, D., Burt, J. R., & Kabakus, I. M. (2025) · Clinical Imaging · doi:10.1016/j.clinimag.2025.110455 · Cited by 13
Development and evaluation of a lightweight large language model chatbot for medication enquiry
Elangovan, K., Ong, J. C. L., Jin, L., Seng, J. J. B., Kwan, Y. H., Ng, L. S., Zhong, R. J., Li, J., Ke, Y. H., Liu, N., et al. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000961 · Cited by 18
Leon, M., Ruaengsri, C., Pelletier, G., Bethencourt, D., Shibata, M., Flores, M. Q., & Shudo, Y. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13216543 · Cited by 12
“Furthermore, the application of ChatGPT in the medical field raises significant ethical risks and challenges, which include concrete concerns related to data privacy, intellectual property contamination, and transparency of model decision-making processes [60].”
BackgroundPasquadibisceglie, V., Appice, A., Malerba, D., & Fiameni, G. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128224 · Cited by 12
Hack, S., Gvili, B., Alsleibi, S., Yogev, D., & Glikson, E. (2025) · European Journal of Plastic Surgery · doi:10.1007/s00238-025-02329-y · Cited by 12
“If relied on without oversight, such outputs could inadvertently harm patients or delay needed care [24–26].”
Large language models for improving cancer diagnosis and management in primary health care settings
Andrew, A., & Tizzard, E. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100157 · Cited by 11
Garcia, A. R. V., Minami, M., Rodríguez, M. M., Ortíz-Morales, J. R., & Radice, F. (2024) · Journal of Orthopaedics · doi:10.1016/j.jor.2024.12.039 · Cited by 11
Vladić, N., Nopp, S., Pabinger, I., Ageno, W., Connors, J. M., Eichinger, S., & Ay, C. (2025) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2025.09.004 · Cited by 10
Grzybowski, A. E., & Jin, K. (2026) · Ophthalmology and Therapy · doi:10.1007/s40123-026-01322-3 · Cited by 12
“Future development directions proposed in the literature include multi-task and multi-disease frameworks, multimodal inputs, and uncertainty quantification, alongside adherence to reporting guidance such as CONSORT-AI and SPIRIT-AI [38, 39].”
Reporting guideline for chatbot health advice studies: The CHART statement
Huo, B., Collins, P. G. S., Chartash, D., Thirunavukarasu, A. J., Flanagin, A., Iorio, A., Cacciamani, G. E., Chen, X., Liu, N., Mathur, P., et al. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103222 · Cited by 11
Medical Artificial Intelligence and Human Values
Manrai, A. K., Yu, K., & Kohane, I. S. (2024) · New England Journal of Medicine · doi:10.1056/nejmc2408971 · Cited by 10
Nazar, W., Nazar, G., Kamińska, A., & Daniłowicz‐Szymanowicz, L. (2025) · Journal of Medical Internet Research · doi:10.2196/70481 · Cited by 13
“In contrast, health care professionals with extensive medical knowledge are better equipped to identify hallucinations and falsifications more easily [40,41,47].”
Cei, F., Ganjavi, C., Layne, E., Hussari, T., Gevorkyan, R., Eppler, M., Piazza, P., Rodler, S., Checcucci, E., Rivas, J. G., et al. (2025) · Minerva Urology and Nephrology · doi:10.23736/s2724-6051.25.06133-6 · Cited by 10
Embracing large language model (LLM) technologies in hydrology research
Ma, Z., Peng, B., Yue, Z., Zeng, H., Pan, M., Wu, X., Yang, J., Mai, L., & Guan, K. (2025) · Environmental Research Water · doi:10.1088/3033-4942/addd43 · Cited by 12
Naghdi, M., Cao, P., Essers, R., Heijligers, M., Paulussen, A. D. C., van der Lugt, A., Ruiter, R. A. C., van Zelst–Stams, W. A. G., Salumets, A., & Esteki, M. Z. (2025) · Human Reproduction · doi:10.1093/humrep/deaf135 · Cited by 9
“…align with broader concerns about the limitations of LLMs in healthcare, including risks such as ‘AI hallucinations’ (de Hond et al. , 2024; Ong et al. , 2024; The Lancet Digital Health, 2024), where models produce incorrect or misleading information, and ‘automation bias’ (Chen et al. ,…”
Dong, C., Qiu, X., Deng, J., Xu, L. L., Dong, X., Chen, S., Mei, T., Li, Q., Cheng, Y., Sun, J., et al. (2025) · Clinical Rheumatology · doi:10.1007/s10067-025-07640-4 · Cited by 9
“This finding is consistent with literature highlighting the challenge of balancing comprehensiveness with precision in LLM-generated medical advice [3, 25].”
Linde, P., Fichter, F., Dietlein, M., Sudbrock, F., Afshar, K., Dapper, H., Fokas, E., Hillebrecht, A., Raupach, T., & Laupichler, M. C. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02313-7 · Cited by 14
Qin, S., Wang, Y., Cui, T., Ma, J., Zhou, X., Guo, X., Zhang, C., Zhou, C., Guo, R., & Li, H. (2025) · Pharmacological Research · doi:10.1016/j.phrs.2025.107883 · Cited by 11
Zhao, W., Lai, H., Pan, B., Huang, J., Xia, D., Bai, C. B., Liu, J., Liu, J., Jin, Y., Shang, H., et al. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1649041 · Cited by 8
Eltaybani, S., Ali, H. F. M., & Abdelhalim, G. E. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104502 · Cited by 9
Socio-Demographic Modifiers Shape Large Language Models’ Ethical Decisions
Sorin, V., Korfiatis, P. D., Collins, J. D., Apakama, D. U., Omar, M., Glicksberg, B. S., Yeow, M.-E., Brandeland, M. A., Nadkarni, G. N., & Klang, E. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00211-x · Cited by 11
Rokhshad, R., Tichý, A., Ducret, M., Chaurasia, A., Uribe, S. E., Gambelin, O., & Schwendicke, F. (2025) · Journal of Dentistry · doi:10.1016/j.jdent.2025.106187 · Cited by 11
Knott, M., Krebs, M., & Kerscher, A. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1761641 · Cited by 9
Optimizing healthcare resource allocation through data-driven demographic and psychographic analysis
Ebugosi, Q.-M. A., & Olaboye, J. A. (2024) · Computer Science & IT Research Journal · doi:10.51594/csitrj.v5i6.1249 · Cited by 8
Ng, M. Y., Helzer, J., Pfeffer, M. A., Seto, T., & Hernandez‐Boussard, T. (2024) · Research Square · doi:10.21203/rs.3.rs-5095287/v1 · Cited by 8
Large Language Models: A Paradigm Shift for Dementia Diagnosis and Care
Harrison, J. R., Tang, S. L., Burston, B., Robertson, A., Liang, H., & Taylor, J. (2025) · British Journal of Hospital Medicine · doi:10.12968/hmed.2024.0666 · Cited by 7
Dundas, N. E., Law, T. J., Brender, T. D., Mills, H., Espejo, E., Heintz, T. A., Wallace, A. W., & Cobert, J. M. (2025) · Anesthesiology · doi:10.1097/aln.0000000000005795 · Cited by 8
BRIDGE: benchmarking large language models for understanding real-world clinical practice texts
Wu, J., Gu, B., Zhou, R., Xie, K., Snyder, D., Jiang, Y., Carducci, V., Wyss, R., Desai, R. J., Alsentzer, E., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01719-2 · Cited by 28
Álvarez-Estapé, M., Cano, I., Pino, R., Grado, C. G., Aldemira-Liz, A., Gonzálvez-Ortuño, J., do Olmo, J., Logroño, J., Martínez, M., Mascías, C., et al. (2024) · medRxiv · doi:10.1101/2024.07.23.24310847 · Cited by 6
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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