Reconsidering Bioethical Principles in the Era of Artificial Intelligence: Challenges for Autonomy, Justice, and Beneficence in Medicine



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Abstract

The widespread integration of artificial intelligence (AI) algorithms into clinical practice, from disease diagnosis to robotic surgery, raises questions about the adequacy of traditional bioethical principles developed for human physician decision-making. This article aimed to assess the applicability of the Beauchamp principles, namely, respect for autonomy, beneficence, and justice, to the realities of AI-mediated medicine and to propose specific strategies for their adaptation. The study involved a systematic review and thematic analysis of international scientific data, clinical cases, and regulatory documents published between 2015 and 2023. The analysis revealed fundamental contradictions. The principle of beneficence is challenged by the black box problem and diffusion of responsibility; autonomy requires revision of informed consent models and codification of the right to explanation; justice is undermined by algorithmic bias; and data confidentiality demands new approaches such as federated learning. Therefore, maintaining trust in medicine requires not the rejection but the evolution of traditional bioethical principles through the incorporation of transparency, accountability, and technical fairness. This indicates the need for new regulatory standards, mandatory algorithmic audits, and the integration of ethical design into the creation of AI-based medical systems.

About the authors

Farida T. Nezhmetdinova

Kazan State Agrarian University

Author for correspondence.
Email: nadgmi@mail.ru
ORCID iD: 0000-0003-2875-128X
SPIN-code: 8441-6943
Scopus Author ID: 55639803900
ResearcherId: F-9660-2014

Cand. Sci. (Philosophy), Assistant Professor

Russian Federation, Kazan

Marina E. Guryleva

Kazan State Medical University

Email: meg4478@mail.ru
ORCID iD: 0000-0003-2772-129X
SPIN-code: 6207-9971
Scopus Author ID: 6602471552

MD, Dr. Sci. (Medicine), Professor

Russian Federation, Kazan

References

  1. Averkin AN, Afanasev SD, Mikryukov AA, et al. Big data standardization: international and national standards. Information society. 2021;(4–5):220–258. doi: 10.52605/16059921_2021_04_220 EDN: DPGURW
  2. Garbuk SV, Shalaev AP. Prospective structure of national standards in the field of artificial intelligence. Standards and quality. 2021;(10):26–33. doi: 10.35400/0038-9692-2021-10-26-33 EDN: IEOWVB
  3. Ethics and Digital: From Problem to Solution. Potapov EG, Shklyaruk MS, editors. Moscow: Russian Presidential Academy of National Economy and Public Administration. 2021. 184 p. (In Russ.)
  4. Nikiforov SV. Legal regulation and formalization of the artificial intelligence’s legal personality in Russian and international law. Gaps in Russian legislation. 2020;(1):79–82. EDN: IXZITB
  5. Mindigulova АA. Ethics and artificial intelligence: problems and contradictions. Meditsina. Sotsiologiya. Filosofiya. Prikladnye issledovaniya. 2022;(3):146–150. EDN: MENBGM
  6. Mittelstadt B. Principles alone cannot guarantee ethical AI. Nature machine intelligence. 2019;1(11):501–507. doi: 10.1038/s42256-019-0114-4 EDN: OAJKFF
  7. Beauchamp TL, James F. Childress Principles of Biomedical Ethics. Oxford University Press, 2001. 454 p.
  8. Nezhmetdinova FT. Bioethics in the context of contemporary scientific strategies and applied ethics in the age of contemporary technologies. Vestnik of Saint Petersburg University. International Relations. 2009;(1):221–229. EDN: KWLOJN
  9. Department of Health, Education, and Welfare; National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report. Ethical principles and guidelines for the protection of human subjects of research. J Am Coll Dent. 2014;81(3):4–13. Available from: https://pubmed.ncbi.nlm.nih.gov/25951677/ Accessed: 01.05.2025.
  10. Rawls D. Theory of justice. Cambridge, Massachusetts: Belknap Press of Harvard University Press; 1971. 538 p.
  11. Nezhmetdinova FT, Guryleva ME. Russian school of bioethics: a quarter of a century of development. Kazan medical journal. 2018;99(3):521–527. doi: 10.17816/KMJ2018-521 EDN: XNKIRV
  12. Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care—Addressing Ethical Challenges. N Engl J Med. 2018;378(11):981–983. doi: 10.1056/NEJMp1714229
  13. Ting Sim JZ, Fong QW, Huang W, Tan CH. Machine learning in medicine: what clinicians should know. Singapore Med J. 2023;64(2):91–97. doi: 10.11622/smedj.2021054 EDN: UKWAOM
  14. Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500–510. doi: 10.1038/s41568-018-0016-5
  15. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14(4):337–339. doi: 10.1016/j.dsx.2020.04.012 EDN: EUGDAX
  16. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–243. doi: 10.1136/svn-2017-000101
  17. Korabelnikov DI, Lamotkin AI. The effectiveness of using artificial intelligence in clinical medicine. Farmakoekonomika. Modern pharmacoeconomics and pharmacoepidemiology. 2025;18(1):114–124. doi: 10.17749/2070-4909/farmakoekonomika.2025.287 EDN: OHRVVR
  18. Chen J, See KC. Artificial Intelligence for COVID-19: Rapid Review. J Med Internet Res. 2020;22(10):e21476. doi: 10.2196/21476
  19. Vvedenskaya EV. Transformation of the physician-patient relationship: from bioethics to roboethics. Human being. 2023;34(6):65–83. doi: 10.31857/S023620070029305-2 EDN: LQEJGB
  20. Tsomartova FV. Robotization in healthcare: legal perspective. Health care of the Russian Federation. 2020;64(2):88–96. doi: 10.46563/0044-197X-2020-64-2-88-96 EDN: ENSOKC
  21. Price WN 2nd, Gerke S, Cohen IG. Potential Liability for Physicians Using Artificial Intelligence. JAMA. 2019;322(18):1765–1766. doi: 10.1001/jama.2019.15064
  22. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. 148 p. License: CC BY-NC-SA 3.0 IGO ISBN: 978-92-4-002920-0
  23. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447–453. doi: 10.1126/science.aax2342
  24. Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018;15(11):e1002689. doi: 10.1371/journal.pmed.1002689
  25. Morley J, Elhalal A, Garcia F, et al. Ethics as a Service: A Pragmatic Operationalisation of AI Ethics. Minds Mach. 2021;31(2):239–256. doi: 10.1007/s11023-021-09563-w EDN: QYBMRI
  26. Bryzgalina EV, Gumarova AN, Shkomova EM. Key problems, risks and restrictions of using artificial intelligence in medicine and education. Moscow university bulletin. Series 7. Philosophy. 2022;(6):93–108. EDN: GXUYWB
  27. Nezhmetdinova FT, Guryleva ME, Blatt NL. New Role of Bioethics in Emergency Situations on the Example of COVID-19. Bionanoscience. 2022;12(2):620–626. doi: 10.1007/s12668-021-00915-5 EDN: UQONAY
  28. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–1930. doi: 10.1161/CIRCULATIONAHA.115.001593 EDN: VFLTDF
  29. Rabbani N, Kim GYE, Suarez CJ, Chen JH. Applications of machine learning in routine laboratory medicine: Current state and future directions. Clin Biochem. 2022;103:1–7. doi: 10.1016/j.clinbiochem.2022.02.011 EDN: TUMKNG
  30. Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to Machine Learning, Neural Networks, and Deep Learning. Transl Vis Sci Technol. 2020;9(2):14. doi: 10.1167/tvst.9.2.14

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