ETHICAL CHALLENGES OF AI DECISION-MAKING IN HEALTHCARE
DOI:
https://doi.org/10.31435/ijitss.1(49).2026.5649Keywords:
Artificial Intelligence, Healthcare Ethics, Clinical Decision-Making, Algorithmic Bias, Explainability, Patient AutonomyAbstract
Artificial intelligence (AI) increasingly supports clinical decision-making in healthcare systems, offering opportunities to improve diagnostic accuracy, treatment planning, and operational efficiency. However, integrating AI technologies introduces significant ethical issues related to algorithmic prejudice, transparency, accountability, patient autonomy, and data governance. This narrative literature review examines ethical issues associated with AI-based decision-making in healthcare by synthesizing contemporary scientific literature. A structured search strategy was applied across major scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar, focusing on publications from 2015 to 2025 that address ethical aspects of AI in clinical contexts. The analysis identified recurring ethical themes, including fairness, explainability, responsibility distribution, and trust in human–AI collaboration. Evidence indicates that although AI enhances evidence-based practice, unresolved ethical risks may affect clinical accountability and patient trust. The study points out the need for evidence-based administrative frameworks and multidisciplinary collaboration to ensure ethical integration of AI technologies into healthcare decision-making procedures.
References
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care: Addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), Article 195.
London, A. J. (2019). Artificial intelligence and black-box medical decisions: Accuracy versus explainability. Hastings Center Report, 49(1), 15–21.
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An initial review of publicly available AI ethics tools. Science and Engineering Ethics, 26(4), 2141–2168.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491–497.
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLOS Medicine, 15(11), e1002689.
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., Ossorio, P. N., Thadaney-Israni, S., & Goldenberg, A. (2019). Do no harm: A roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337–1340.
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731.
Uwishema, O., Ghezzawi, M., Charbel, N., Alawieh, S., Roy, S., Wojtara, M., Hakayuwa, C. M., Ja’afar, I. K., Nkurunziza, G., & Prasad, M. (2025). Diagnostic performance of artificial intelligence for dermatological conditions: A systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care. International Journal of Emergency Medicine, 18(1). https://doi.org/10.1186/s12245-025-00975-4
Elgazzar, K., Wadie, P., Eissa, C., Alsbakhi, A., & Alhejaily, A. M. G. (2025). AI-driven innovations in diagnostics, remote monitoring, and clinical decision support systems: A systematic review [Preprint]. https://doi.org/10.2196/preprints.80928
Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2010). The digital transformation of healthcare: Current status and the road ahead. Information Systems Research, 21(4), 796–809. https://doi.org/10.1287/isre.1100.0327
Bates, D. W., Cohen, M., Leape, L. L., Overhage, J. M., Shabot, M. M., & Sheridan, T. (2001). Reducing the frequency of errors in medicine using information technology. Journal of the American Medical Informatics Association, 8(4), 299–308. https://doi.org/10.1136/jamia.2001.0080299
Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517–518. https://doi.org/10.1001/jama.2017.7797
Ekeland, A. G., Bowes, A., & Flottorp, S. (2010). Effectiveness of telemedicine: A systematic review. International Journal of Medical Informatics, 79(11), 736–771. https://doi.org/10.1016/j.ijmedinf.2010.08.006
Gagnon, M. P., Nsangou, É. R., Payne-Gagnon, J., & Grenier, S. (2016). Barriers and facilitators to implementing electronic health records. Journal of Medical Systems, 40(12), 1–8. https://doi.org/10.1007/s10916-016-0628-9
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A’Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017). Beyond adoption: A new framework for theorizing digital health technologies. Journal of Medical Internet Research, 19(11), e367. https://doi.org/10.2196/jmir.8775
Hollander, J. E., & Carr, B. G. (2020). Virtually perfect? Telemedicine for COVID-19. New England Journal of Medicine, 382(18), 1679–1681. https://doi.org/10.1056/NEJMp2003539
Kruse, C. S., Kristof, C., Jones, B., Mitchell, E., & Martinez, A. (2016). Barriers to electronic health record adoption. JMIR Medical Informatics, 4(2), e19. https://doi.org/10.2196/medinform.4843
Lupton, D. (2014). Critical perspectives on digital health technologies. Sociology Compass, 8(12), 1344–1359. https://doi.org/10.1111/soc4.12226
Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation management. MIS Quarterly, 41(1), 223–238.
OECD. (2019). Health in the 21st century: Putting data to work for stronger health systems. OECD Publishing.
Porter, M. E., & Lee, T. H. (2013). The strategy that will fix healthcare. Harvard Business Review, 91(10), 50–70.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
World Health Organization. (2021). Global strategy on digital health 2020–2025. WHO.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Adam Wiktor Rożenek, Marta Kołodziej-Sieradz, Hubert Jarosław Ćwiek, Paulina Klaudia Gryz, Anna Aleksandra Szwankowska, Anna Baczyńska, Błażej Boruszczak, Anna Magdalena Terlecka, Karolina Jolanta Pilarska, Kacper Komorowski
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
How to Cite
