Copyright: © 2025 by the authors. Licensee: Pirogov University.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (CC BY).

OPINION

Large language models in medicine: current ethical challenges

About authors

Yaroslavl State Medical University, Yaroslavl, Russia

Correspondence should be addressed: Sergey A. Kostrov
: Revolutsionnaya str., 5., Yaroslavl, 150000, Russia; ur.umsy@aesok

About paper

Author contribution: Potapov MP — research planning, analysis, editing; Kostrov SA — collection, analysis, interpretation of data, preparation of a draft manuscript.

Received: 2025-05-06 Accepted: 2025-05-20 Published online: 2025-06-29
|
  1. Meng X, Yan X, Zhang K, et al. The application of large language models in medicine: A scoping review. iScience. 2024; 27(5): 109713.
  2. Omiye JA, Gui H, Rezaei SJ, Zou J, Daneshjou R. Large Language Models in Medicine: The Potentials and Pitfalls: A Narrative Review. Ann Intern Med. 2024; 177(2): 210–220. DOI: 10.7326/M23-2772.
  3. Chen Y, Esmaeilzadeh P. Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges. J Med Internet Res. 2024; 26: e53008. DOI: 10.2196/53008.
  4. Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform. 2024; 12: e52073. DOI: 10.2196/52073.
  5. Nógrádi B, Polgár TF, Meszlényi V, et al. ChatGPT M. D. Is there any room for generative AI in neurology? PLoS One. 2024; 19(10): e0310028. DOI: 10.1371/journal.pone.0310028.
  6. Wang C, Li M, He J, Wang Z, Darzi E, Chen Z, et al. A Survey for Large Language Models in Biomedicine. ArXiv. 2024; abs/2409.00133.
  7. Moglia A, Georgiou K, Cerveri P, et al. Large language models in healthcare: from a systematic review on medical examinations to a comparative analysis on fundamentals of robotic surgery online test. Artif Intell Rev. 2024; 57: 231. DOI: 10.1007/s10462-024-10849-5.
  8. Ong JCL, Chang SY, William W, et al. Ethical and regulatory challenges of large language models in medicine. Lancet Digit Health. 2024; 6(6): e428-e432. DOI: 10.1016/S2589-7500(24)00061-X.
  9. Zhui LL, Fenghe L, Xuehu W, Qining F, Wei R. Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint. J Med Internet Res. 2024; 26: e60083. DOI: 10.2196/60083
  10. Wei Y, Zhou J, Wang Y, et al. A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications. IEEE Trans Biomed Circuits Syst. 2020; 14(2): 145–163. DOI: 10.1109/TBCAS.2020.2974154
  11. Pikalov Ya S. Obzor arkhitektur sistem intellektual’noy obrabotki yestestvenno-yazykovykh tekstov. Problemy iskusstvennogo intellekta. 2020; 4(19). Russian.
  12. Jin L, Feng S, Xin Z, Chai Y. Evolution and advancements in deep learning models for Natural Language Processing. Applied and Computational Engineering. 2024.
  13. Li K, Ao B, Wu X, Wen Q, Ul Haq E, Yin J. Parkinson’s disease detection and classification using EEG based on deep CNN-LSTM model. Biotechnol Genet Eng Rev. 2024; 40(3): 2577–2596. DOI: 10.1080/02648725.2023.2200333
  14. Haltaufderheide J, Ranisch R. The ethics of ChatGPT in medicine and healthcare: a systematic review on Large Language Models (LLMs). NPJ Digit Med. 2024; 7(1): 183. Published 2024 Jul 8. DOI: 10.1038/s41746-024-01157-x.
  15. Garcez AA, et al. Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. arXiv preprint arXiv:1905.06088. 2019.
  16. Li Z, et al. From system 1 to system 2: A survey of reasoning large language models. arXiv preprint arXiv: 2502.17419. 2025.
  17. Cascella M, Semeraro F, Montomoli J, Bellini V, Piazza O, Bignami E. The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives. J Med Syst. 2024; 48(1): 22. DOI: 10.1007/s10916-024-02045-3.
  18. Andreychenko AYe., Gusev AV. Perspektivy primeneniya bol’shikh yazykovykh modeley v zdravookhranenii. Natsional’noye zdravookhraneniye. 2023; 4 (4): 48–55. DOI: 10.47093/2713-069X.2023.4.4.48-55. Russian.
  19. Moffatt B, Hall A. Is AI my co-author? The ethics of using artificial intelligence in scientific publishing. Account Res. 2024. DOI: 10.1080/08989621.2024.2386285.
  20. Lee JY. Can an artificial intelligence chatbot be the author of a scholarly article? J Educ Eval Health Prof. 2023; 20: 6. DOI: 10.3352/jeehp.2023.20.6.
  21. Johnson A. Generative AI, UK Copyright and Open Licences: considerations for UK HEI copyright advice services. F1000Res. 2024; 13: 134. DOI: 10.12688/f1000research.143131.1.
  22. Shaydurov AS. Avtorskoye pravo na proizvedeniya, sozdannyye iskusstvennym intellektom v RF: problemy i perspektivy. V sbornike: Tsifrovyye tekhnologii v nauchnom razvitii: novyye kontseptual’nyye podkhody: Sbornik statey po itogam Mezhdunarodnoy nauchno-prakticheskoy konferentsii; 30 aprelya 2023 g.; Samara. Sterlitamak: Obshchestvo s ogranichennoy otvetstvennost’yu «Agentstvo mezhdunarodnykh issledovaniy». 2023; 88–92. EDN MDZVBE. Russian.
  23. Kishkembayev M. Aktual’nyye problemy zashchity avtorskikh prav na ob”yekt, sozdannyy iskusstvennym intellektom. Vestnik Torajgyrov Universiteta. Ûridičeskaâ Seriâ. Mart 2024; 75–86. DOI: 10.48081/zusz1934. Russian.
  24. Avila Negri SMC. Robot as Legal Person: Electronic Personhood in Robotics and Artificial Intelligence. Front Robot AI. 2021;8:789327. Published 2021 Dec 23. DOI: 10.3389/frobt.2021.789327.
  25. Schmidgall S, Harris C, Essien I, et al. Evaluation and mitigation of cognitive biases in medical language models. NPJ Digit Med. 2024; 7(1): 295. DOI: 10.1038/s41746-024-01283-6.
  26. Mondal M, et al. Do large language models exhibit cognitive dissonance? studying the difference between revealed beliefs and stated answers. Preprint arXiv. 2406.14986. 2024.
  27. Goerlandt F, Li J, Reniers G. The Landscape of Risk Communication Research: A Scientometric Analysis. International Journal of Environmental Research and Public Health. 2020; 17(9): 3255. DOI: 10.3390/ijerph17093255.
  28. Zarfati M, Soffer S, Nadkarni GN, Klang E. Retrieval-Augmented Generation: Advancing personalized care and research in oncology. Eur J Cancer. 2025; 220: 115341. DOI: 10.1016/j.ejca.2025.115341.
  29. Shool S, Adimi S, Saboori Amleshi R, Bitaraf E, Golpira R, Tara M. A systematic review of large language model (LLM) evaluations in clinical medicine. BMC Med Inform Decis Mak. 2025; 25(1): 117. DOI: 10.1186/s12911-025-02954-4.
  30. Shevskaya NV. Ob”yasnimyy iskusstvennyy intellekt i metody interpretatsii rezul’tatov. Modelirovaniye, optimizatsiya i informatsionnyye tekhnologii. 2021; 9(2). DOI: 10.26102/2310-6018/2021.33.2.024. EDN VRKUIL. Russian.
  31. Xiong G, et al. Benchmarking retrieval-augmented generation for medicine. Findings of the Association for Computational Linguistics ACL. 2024. 2024; 6233–6251.
  32. Tang X et al. Medagentsbench: Benchmarking thinking models and agent frameworks for complex medical reasoning. arXiv preprint arXiv:2503.07459. 2025.
  33. Yadav N, Pandey S, Gupta A, Dudani P, Gupta S, Rangarajan K. Data Privacy in Healthcare: In the Era of Artificial Intelligence. Indian Dermatol Online J. 2023;14(6): 788–792. DOI: 10.4103/idoj.idoj_543_23.
  34. Altynnikov MS, Kuznetsova NO. Osobennosti organizatsii zashchity personal’nykh dannykh v meditsinskom uchrezhdenii. Innovatsii. Nauka. Obrazovaniye. 2021; 36: 1479–1486. EDN ZISIGQ. Russian.
  35. Prikaz Minzdrava Rossii ot 20.03.2025 № 139n «Ob utverzhdenii Poryadka obezlichivaniya svedeniy o litsakh, kotorym okazyvayetsya meditsinskaya pomoshch’, a takzhe o litsakh, v otnoshenii kotorykh provodyatsya meditsinskiye ekspertizy, meditsinskiye osmotry i meditsinskiye osvidetel’stvovaniya». Ofitsial’nyy internet-portal pravovoy informatsii. 2025. Russian.
  36. Wiest IC, et al. Anonymizing medical documents with local, privacy preserving large language models: The LLM-Anonymizer. medRxiv. 2024. С. 2024.06. 11.24308355.
  37. Morris JX, et al. Text embeddings reveal (almost) as much as text. arXiv preprint arXiv:2310.06816. 2023.
  38. Mascalzoni D, Melotti R, Pattaro C, et al. Ten years of dynamic consent in the CHRIS study: informed consent as a dynamic process. Eur J Hum Genet. 2022;30: 1391–1397 DOI: 10.1038/s41431-022-01160-4. 2022.
  39. Harishbhai Tilala M, Kumar Chenchala P, Choppadandi A, et al. Ethical Considerations in the Use of Artificial Intelligence and Machine Learning in Health Care: A Comprehensive Review. Cureus. 2024; 16(6): e62443. Published 2024 Jun 15. DOI: 10.7759/cureus.62443.
  40. Postanovleniye Pravitel’stva RF ot 18.07.2023 № 1164 (red. ot 01.02.2025) «Ob ustanovlenii eksperimental’nogo pravovogo rezhima v sfere tsifrovykh innovatsiy i utverzhdenii Programmy eksperimental’nogo pravovogo rezhima v sfere tsifrovykh innovatsiy po napravleniyu meditsinskoy deyatel’nosti, v tom chisle s primeneniyem telemeditsinskikh tekhnologiy i tekhnologiy sbora i obrabotki svedeniy o sostoyanii zdorov’ya i diagnozakh grazhdan». Ofitsial’nyy internet-portal pravovoy informatsii. 2025. Russian.
  41. Lessage X, Collier L, Van Ouytsel C-HB, Legay A, Mahmoudi S and Massonet P. Secure federated learning applied to medical imaging with fully homomorphic encryption. 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC). Houston. TX. USA. 2024; 1–12. DOI: 10.1109/ICAIC60265.2024.10433836.
  42. Walskaar, I, Tran, MC, & Catak, FO. A Practical Implementation of Medical Privacy-Preserving Federated Learning Using Multi-Key Homomorphic Encryption and Flower Framework. Cryptography. 2023; 7(4): 48. DOI: 10.3390/cryptography7040048.
  43. Mohandas R, Veena S, Kirubasri G. Thusnavis Bella Mary I & Udayakumar, R. Federated Learning with Homomorphic Encryption for Ensuring Privacy in Medical Data. Indian Journal of Information Sources and Services. 2024; 14(2): 17–23. DOI: 10.51983/ijiss-2024.14.2.03.
  44. Shumway DO, Hartman HJ. Medical malpractice liability in large language model artificial intelligence: legal review and policy recommendations. J Osteopath Med. 2024; 124(7): 287–290. DOI: 10.1515/jom-2023-0229.
  45. Tret’yakova Ye P. Ispol’zovaniye iskusstvennogo intellekta v zdravookhranenii: raspredeleniye otvetstvennosti i riskov. Tsifrovoye pravo. 2021; 2(4): 51–60. DOI: 10.38044/2686-9136-2021-2-4-51-60. EDN NGHUTA. Russian.
  46. Mazhuga YeYu, Petrova AO, Gordeyev Ya I. Pravovoye regulirovaniye sistem iskusstvennogo intellekta v meditsinskoy sfere. V sbornike: Aktual’nyye problemy sovremennoy Rossii: psikhologiya, pedagogika, ekonomika, upravleniye i pravo. Sbornik nauchnykh trudov mezhdunarodnykh nauchno-prakticheskikh konferentsiy; 07–24 aprelya 2023 g.; Moskva. Moskva: Moskovskiy psikhologo-sotsial’nyy universitet. 2023; 1281–1285. EDN IAGUFA. Russian.