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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
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.
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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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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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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
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Yousefi, N., & Zamanifar, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-15088-2_10 · Cited by 1
Fink, M. A., Bischoff, A., Atsiatorme, E., Kremer, A., Kroschke, J., Moll, M., Stein, P., Riebl, V., Leichenich, T., Kauczor, H., et al. (2026) · Insights into Imaging · doi:10.1186/s13244-026-02289-2 · Cited by 1
“Proprietary LLMs have shown strong performance, but concerns remain regarding data governance, reproducibility, and recurring access costs [14, 15].”
Ong, A. Y., Merle, D. A., Shah, N. H., Tham, Y. C., Wong, T. Y., & Keane, P. A. (2026) · The Lancet Digital Health · doi:10.1016/j.landig.2026.100982 · Cited by 3
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Shin, C., Park, J. H., Kim, S. J., Lee, Y. H., & Lee, H. S. (2026) · Healthcare · doi:10.3390/healthcare14111490 · Cited by 2
“In particular, managing the inherent ‘plas-ticity’ of LLMs is essential to prevent stochastic shifts that can lead to biased or inconsistent clinical outputs [17].”
Jeong, I. H., Kim, H., & Hwang, H. (2026) · Kosin Medical Journal · doi:10.7180/kmj.26.173 · Cited by 1
“…in competency judgments, intellectual property concerns related to AI-generated educational materials, and transparency requirements for AI-assisted assessments, require coordinated multidisciplinary engagement among legal scholars, ethicists, policymakers, and medical education researchers [64].”
Lott, J., Stubbert, B., McShannon, D., Bellissimo, J., Patel, D., & Dietrich, N. T. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106573 · Cited by 1
A roadmap for medical large language models: a review of foundations, applications, and challenges
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Kim, Y., Kim, J., Cho, Y., & Choi, M. (2026) · BMC Nursing · doi:10.1186/s12912-026-04949-6 · Cited by 1
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Zhou, J., Zhou, Q., Zhou, L., Wang, J., Gao, J., Qin, L., Tan, B., Fan, P., & Li, C. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1921819 · Cited by 1
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Toma, M., & YUSUPOV, D. (2026) · Bioengineering · doi:10.3390/bioengineering13091034 · Cited by 1
Hanna, D. R., Creswell, M. L., Terry, R. S., Vergamini, L. B., Sardiu, M. E., Du, H. E., McMahon, A. K., Molina, W., & Whiles, B. B. (2025) · World Journal of Urology · doi:10.1007/s00345-025-05533-4 · Cited by 1
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de Lima, J. B. C., Griffin, I., Gemmell, J. S., Davis, K., Amanamba, U., Zanjani, N. A., Siyanaki, M. R. H., Mohammed, T.-L. H., Benos, T., Rodrigues, R. S., et al. (2025) · Seminars in Roentgenology · doi:10.1053/j.ro.2025.06.003 · Cited by 2
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Yap, J. A., & Weber, M. D. (2025) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2561-6948 · Cited by 1
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“To minimize the randomness in outputs, we set the temperature to 0.0 and initialized a fixed random seed.”
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Jin, K., Shi, D., & Zhang, J. (2026) · BMJ Open Ophthalmology · doi:10.1136/bmjophth-2026-002745 · Cited by 1
Guo, Z., Mao, H., & Zhang, F. (2026) · Publications · doi:10.3390/publications14020027
“Second, it may erode academic credibility as the boundary between authentic scholarship and machine-generated output becomes increasingly blurred (Ong et al., 2024).”
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Anibal, J., Gunkel, J., Awan, S. N., Huth, H. B., Nguyen, H. T. T., Le, T., Bélisle‐Pipon, J., Boyer, M., Hazen, L., Consortium, B. V., et al. (2024) · PubMed · doi:10.48550/arxiv.2408.07896
Phillips, V. J., Rao, N. L., Sanghvi, Y., & Nizam, M. (2024) · Cureus · doi:10.7759/cureus.74876
Lee, H.-J., & Buu, S.-J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5133830
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Zhao, K., Chen, L., & Sun, Z. (2025) · The Lancet · doi:10.1016/s0140-6736(24)02855-1
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Shaari, A. L., Saad, A. M., Patil, D. P., Yanik, J., Hsueh, W. D., Eloy, J. A., & Filimonov, A. (2025) · Laryngoscope Investigative Otolaryngology · doi:10.1002/lio2.70130 · Cited by 1
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Johnson, H. H., Beck, R., Li, W., Pateman, A., & Sediqi, K. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93415-5_2 · Cited by 1
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Ray, P. P. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf299
“For instance, the study’s suggestion of a mixture of experts (MoE) strategy, which dynamically routes tasks to specialized sub-models, offers a promising avenue for mitigating current limitations and ensuring the adaptability and reliability of LLM outputs in complex regulatory contexts [2, 3].”
Edwards, A., Pardiñas, A. F., Kirov, G., Rees, E., & Camacho-Collados, J. (2025) · JMIR AI · doi:10.2196/72256 · Cited by 2
Morley, J. (2025) · Wiley eBooks · doi:10.1002/9781394240821.ch13
Mukisa, R., & Matovu, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92611-2_3
Yao, R., Qu, Y., Zhang, J., Zhou, X., Lian, J., Wu, A., Pan, B., Zhou, Y., & Sun, Q. (2025) · doi:10.2196/preprints.81674
Kalyan Chakravarthy, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5393117
Wu, P., Li, F.-X., Jia, Y., Yin, J., Shen, Y., Gao, Y., Li, Y., Sha, F., Yang, Z., & Tang, J. (2025) · Gastroenterology & Endoscopy · doi:10.1016/j.gande.2025.09.003
Levita, B., Eminovic, S., Lüdemann, W. M., Schnapauff, D., Schmidt, R., Haack, A.-M., Dell’Orco, A., Nawabi, J., & Penzkofer, T. (2025) · CVIR Endovascular · doi:10.1186/s42155-025-00609-z
“Determining responsibility in cases of patient harm is particularly complex [43], and current data collection methods pose additional threats to patient confidentiality and privacy [44].”
Becker, P., & Serf, G. P. (2025) · Springer eBooks · doi:10.1007/978-3-658-48573-3_4
Chanci, D., Moore, R., Foote, H. P., Goldstein, M. A., Kumar, K. R., Rotta, A. T., Hornik, C. P. V., Burriss‐West, M., Hamilton, M., Stanco, N., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-57353-0
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Kühn, S., & Knitza, J. (2025) · Osteologie/Osteology · doi:10.1055/a-2697-4943
Li, L., Su, H., Li, P., & Cao, J. (2025) · Asian Journal of Surgery · doi:10.1016/j.asjsur.2025.11.124
Ethical and regulatory of artificial intelligence in drug design
Luo, X., Chen, F., Chen, Y., & Zhou, Q. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-332908-1.00032-5
Kewan, T. Z., Lee, A., & Doren, L. V. (2025) · doi:10.2196/preprints.89939
Mukku, L., & Burri, V. (2025) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2025.104824
Jabłoński, K., Grygorowicz, A., Baran, K., Ględa, M., Szyszka, M., Radecka, W., Kozak, W., Szreiber, A., Grela, K., Nowacka, K., et al. (2025) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.4(48).2025.4740
Goundrey-Smith, S. (2026) · Health informatics · doi:10.1007/978-1-4471-7613-8_1
Acceleration AI Ethics and the Telus GenAI Conversational AgentPreprint
Brusseau, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6123637
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Brusseau, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6123639
Adebayo, U. O., Okesanya, O. J., Obadeyi, K. B., Ayelaagbe, O. B., Ahmed, M. M., & Lucero-Prisno, T. A. O. A. D. E., III (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1014204
Pei, L., Hu, J., Wanqing, Z., Wang, Q., & Fang, A. (2026) · doi:10.2196/preprints.92241
Kewan, T. Z., Lee, A., & Doren, L. V. (2026) · JMIR Formative Research · doi:10.2196/89939
“Finally, developing practical guidelines for human-AI integration is essential as educational and clinical environments adopt these tools [8,9].”
He, Q., Shi, J., Liu, X., Tian, F., Bu, S., & Jiang, Y. (2026) · iScience · doi:10.1016/j.isci.2026.115002
“15,16 These include concerns regarding accuracy, accessibility, and readability of AI-driven health communication.”
AI Tools for Writing and Editing in Academia and Research
Lavania, S., Verma, A. K., & Basuel, R. B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5601-3.ch014
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Lim, Y. J., & Tan, N. C. (2026) · Annals of the Academy of Medicine Singapore · doi:10.47102/annals-acadmedsg.2025220
Badilla-Ramos, K. (2026) · Alfinge Revista de Filología · doi:10.21071/arf.v37i.18656
Shenoi, A., Li, T., Jabir, A. I., Pitkethly, A. J., Fleisch, E., Kowatsch, T., & Mair, J. L. (2026) · doi:10.2196/preprints.94036
A detailed review of large language models in the healthcare sector
Ozsahin, D. U., Emegano, D. I., Uzun, B., & Özsahin, İ. (2026) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-026-00327-z
“Finally, the regulation of these frameworks, as suggested by Kucharavy et al., and Ong et al. [77, 78] is crucial for data privacy and ensuring adherence to ethical standards like HIPAA.”
Generative AI and chatbots in social media interactions on young persons
Johnson, S. (2026) · Neuropsychiatrie de l Enfance et de l Adolescence · doi:10.1016/j.neurenf.2026.02.001
Ibitoye, A., Johnson, D. R., Sorinolu, B. G., Orji, R., & Abikoye, O. C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01072-9
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Safarov, R., Wippermann, J., & Wacker, M. (2026) · Zeitschrift für Herz- Thorax- und Gefäßchirurgie · doi:10.1007/s00398-026-00763-z
Singh, A., Bhardwaj, A., Jena, A., Midha, V., Sood, A., Sharma, V., & Sebastian, S. (2026) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjag019
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Chua, D. Y., McMahon, B., & Kos, S. (2026) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2026.100352
Ek, J. E., & Kiebooms, E. (2026) · Artificial Intelligence in Emergency Medicine · doi:10.1016/j.aiemed.2026.100008
Kermansaravi, M., Pantelis, A. G., Torensma, B., Shahabi, S., Mazzarella, M., D’Arco, S., Lee, Y. S., Okrainec, A., Leite, S., Shin, T. H., et al. (2026) · Obesity Surgery · doi:10.1007/s11695-026-08578-5
Luo, W., Feng, T., Zhang, T., Chen, X., Lu, X., Li, Y., Hou, C., & Gao, J. (2026) · Public Health · doi:10.1016/j.puhe.2026.106252
Li, H., Cao, J., Duan, S., Hu, S., Lou, L., Lin, M., Jian, T., Shao, J., Zhang, X., Chen, P., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02510-y · Cited by 1
“The use of facial video data in clinical AI systems raises heightened concerns regarding patient privacy and ethical responsibility 29 – 31 .”
Language-dependent variability in large language model performance on pharmaceutical knowledge tasksPreprint
Asano, H., Tian, Y., Hatabu, A., Ohishi, M., Fukuzawa, K., Takaya, D., & Ikeda, K. (2026) · Research Square · doi:10.21203/rs.3.rs-9217016/v1
Ejiyi, C. J., Obiora, S. C., Obiora, I., Wauk, G., Ejiako, M. O., Omotayo, T., & Bamisile, O. (2026) · Future Internet · doi:10.3390/fi18040196 · Cited by 2
Zhang, F., Deng, X., Zou, X., Ou, Y., Tang, R., Tang, L., Li, Y., Li, Y., Tang, Q., Liu, R., et al. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103137
Doarn, C. R. (2026) · Telemedicine Journal and e-Health · doi:10.1177/15305627261444824
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Dobranski, N., Viscariello, N. N., Pitcher, G. M., Stam, A. M., Belliveau, J., Kirby, K. M., Solis, D. E., Ashford, J., Spears, H., Dalton, A., et al. (2026) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2026.05.003
Li, J., Wu, K., Wang, X., Tao, R., Ma, Y., Lv, H., Zhao, X., Lan, Y., Li, L., & Wang, L. (2026) · BioData Mining · doi:10.1186/s13040-026-00562-0
“Yet these approaches introduce practical challenges for typical clinical researchers: many rely on full-text access, cloud-hosted APIs, or manually curated knowledge graphs, raising concerns about patient privacy, copyright, and reproducibility, and making local deployment difficult [14, 15].”
Chong, A., Lim, C. H., Kim, D., Yun, M., Bom, H.-S. H., & Lee, S. H. (2026) · Nuclear Medicine and Molecular Imaging · doi:10.1007/s13139-026-01029-0 · Cited by 1
“As noted in recent literature, the integration of AI into image-related workflows necessitates clear insti - tutional policies regarding de-identification, third-party data processing, and regulatory compliance [ 3, 13].”
Gaetano, D., Lomonaco, V., Ferraioli, S., Pota, V., Colella, U., Pace, M. C., & Sansone, P. (2026) · BMC Anesthesiology · doi:10.1186/s12871-026-03937-2
“They also echo prior concerns about safety, legal liability, and algorithmic opacity [23], even though these were not the primary focus of our questionnaire.”
InfluentialAli, A., Kumar, K., & Kaur, H. (2026) · International Conference on Intelligent Processing, Hardware, Electronics, and Radio Systems (CIPHER) · doi:10.1109/cipher70417.2026.11523731
“Responsible clinical adoption will not be achieved by technology advancement alone [5] [6] [16].”
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Shenoi, A., Li, T., Jabir, A. I., Pitkethly, A. J., Fleisch, E., Kowatsch, T., & Mair, J. L. (2026) · JMIR Formative Research · doi:10.2196/94036
Zhu, J., Fan, Y., Han, G., Shi, X., Deng, F., & Chen, J. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11591-w
Starke, G. (2026) · Ethik in der Medizin · doi:10.1007/s00481-026-00922-8
“Einschlägige Beispiele liefern insbesondere Anwendungen großer Sprachmodelle ( large language models, LLMs) in der Medizinethik, die als eine besondere Spielart künstlicher Intelligenz (KI) in den vergangenen Jahren zunehmend prominent und kontrovers diskutiert wurden (Ong et al. 2024).”
Die Rolle von Large Language Modellen in der wissenschaftlichen Recherche
Lindner, T., Weber, M.-A., & Manzke, M. (2026) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-2868-7797
Medicine—Engineering Integration in General Practice: Current Status and Future Perspectives
Xu, Z. (2026) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-026-2935-8
“Addressing these issues requires robust regulatory frameworks and ongoing ethical oversight [31 , 32] .”
Security, Privacy, and Ethical Challenges in Large Language Models: A Review
Yogi, B., Ghosh, N., & Sahid, S. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10680-x
Yıldırım, M. S., DEMIRAY, T. D., Ayten, A., & Kaya, E. K. (2026) · Sakarya Medical Journal · doi:10.31832/smj.1842543
Ethical Impact of AI Systems in Healthcare: A Survey of Recorded Incidents
Tuovinen, L. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28819-6_27
Large language model-based diabetes continuation care intervention: A scoping review
Fu, J., Lin, J., Zhang, Y., Cai, S., & Li, M. (2026) · Interdisciplinary Nursing Research · doi:10.1097/nr9.0000000000000123
Mahmud, K., Kumar, M., Singh, D., & Saini, R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44344-2.00022-4
Jebbeh, R. A., & Alnaeem, M. M. (2026) · Applied Nursing Research · doi:10.1016/j.apnr.2026.152113
A Commentary on Ethics Case Reflections as an Ethics laboratory in Clinical Practice
Bennesved, A., Bremer, A., & Björk, J. (2026) · HEC Forum · doi:10.1007/s10730-026-09598-5
Alkam, T., Tarshizi, E., & Benschoten, A. H. V. (2026) · Journal of dementia and Alzheimer's disease · doi:10.3390/jdad3040048
Saha, D., Lin, Y., & Ghose, D. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3828166
Plastrier, K. L., Anand, K., & Smith, J. W. (2026) · doi:10.2196/preprints.105656
Technologies, Applications, and Challenges of Large Language Models
Ahmad, I., Amelio, A., Aracne, M., Caroprese, L., Gill, E. Z., & Christian Morbidoni, B. (2026) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro70003.2026.11591567 · Cited by 1
“In healthcare, LLMs are considered unsafe for unsupervised clinical decisions due to foreseeable and serious errors [23], and this concern aligns with medical malpractice analyses that anticipate shared liability between clinicians and vendors, reinforced by validation requirements and continuous…”
Ocak, D. (2026) · AI and Ethics · doi:10.1007/s43681-026-01273-2
“The recommendations that follow adapt the ethical and regulatory imperatives emerging in the LLM-in-medicine literature to the specific phenomenological vulnerabilities of psychiatric practice [ 18 ].”
Descamps, J., Bouguennec, N., Porcher, R., Resche‐Rigon, M., & Bouché, P.-A. (2026) · Orthopaedics & Traumatology Surgery & Research · doi:10.1016/j.otsr.2026.104793
Gupta, D., Khamidova, M., Norov, J., Rakhmonkulova, F., Gaffarovich, N. M., Ashirova, A. I., & Bayramovich, S. B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0368-8.ch001
Benchmarking large language models for de-identification of electronic health record notes
Panchal, O., Chang, N., Zhao, Z., Nadar, D. R., Dai, H.-J., & Jonnagaddala, J. (2026) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2025-101894
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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