Artificial Intelligence Ethics In Healthcare Management: A Review Of Patient Privacy, Bias, And Transparency

Abstract

The rapid integration of artificial intelligence (AI) into healthcare management has transformed efficiency, resource use, and patient care, but also introduced ethical challenges regarding privacy, bias, and transparency. AI’s reliance on sensitive data for predictive analytics and decision support increases risks of breaches and unauthorized sharing, highlighting the need for privacy-preserving methods like federated learning and differential privacy. Algorithmic bias, often rooted in non-representative datasets or flawed designs, can perpetuate healthcare inequities, underscoring inclusive data practices and auditing. The opacity of deep learning models undermines trust and accountability, reinforcing the importance of explainable AI and standardized reporting. Current gaps include the absence of consistent ethical metrics and scalable solutions. Future directions involve global governance frameworks and stakeholder engagement. Overall, a holistic strategy integrating technical, organizational, and regulatory approaches is essential for equitable and trustworthy AI in healthcare.

Keywords

• Artificial Intellegente
• Healthcare Management
• Patient Privacy

Sağlık Yönetiminde Yapay Zekânın Etik Boyutları: Hasta Gizliliği, Algoritmik Önyargı ve Şeffaflık Üzerine Bir Derleme

Öz

Yapay zekânın (YZ) sağlık yönetiminde hızla yaygınlaşması, operasyonel verimlilik, kaynak tahsisi ve hasta bakımını dönüştürmüştür. Ancak bu entegrasyon; hasta gizliliği, algoritmik önyargı ve şeffaflık gibi önemli etik zorlukları gündeme getirmektedir. YZ’nin tahmine dayalı analitikler ve karar destek sistemlerinde hassas verilere bağımlılığı, veri ihlali risklerini artırmakta; federatif öğrenme ve diferansiyel gizlilik gibi yöntemlerin gerekliliğini göstermektedir. Algoritmik önyargı, temsili olmayan verilerden kaynaklanarak eşitsizlikleri sürdürebilir; bu nedenle kapsayıcı veri ve algoritmik denetim kritik önemdedir. Derin öğrenme gibi opak modeller ise güveni zedelemekte, açıklanabilir YZ ve standart raporlama protokolleri ihtiyacını ortaya koymaktadır. Literatürden elde edilen bulgular, etik ölçütlerin eksikliği, küresel yönetişim çerçeveleri ve paydaş katılımının gerekliliğini vurgulayarak adil, şeffaf ve güvenilir YZ için bütüncül stratejilere işaret etmektedir.

Anahtar Kelimeler

• Yapay Zeka
• Sağlık Yönetimi
• Hasta Gizliliği

Kaynakça

1. Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J., & Gao, G. (2023). Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework. Health Policy and Technology, 12(1), 100702.
2. Al-Amiery, A. (2025). The ethical implications of emerging artificial intelligence technologies in healthcare. Med Mat, 2(2), 85-100.
3. Altamirano, S., Vreeken, A., & Ghebreab, S. (2025, October). Machine Learning and Public Health: Identifying and Mitigating Algorithmic Bias through a Systematic Review. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (Vol. 8, No. 1, pp. 138-151).
4. Aquino, Y. S. J., Carter, S. M., Houssami, N., Braunack-Mayer, A., Win, K. T., Degeling, C., ... & Rogers, W. A. (2025). Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives. Journal of Medical Ethics, 51(6), 420-428.
5. Bosco, C. M., Chin, M. H., & Parker, W. F. Addressing Bias in Clinical Algorithms to Advance Health Equity.
6. Bouderhem, R. (2024). Shaping the future of AI in healthcare through ethics and governance. Humanities and social sciences communications, 11(1), 1-12.
7. Gorelik, A. J., Li, M., Hahne, J., Wang, J., Ren, Y., Yang, L., ... & Carpenter, B. D. (2025). Ethics of AI in healthcare: a scoping review demonstrating applicability of a foundational framework. Frontiers in
8. Digital Health, 7, 1662642. https://doi.org/10.3389/fdgth.2025.1662642

9. El-Geneedy, M., El-Din Moustafa, H., Khater, H., Abd-Elsamee, S., & Gamel, S. A. (2025). A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction. Scientific Reports, 15(1), 26048. https://doi.org/10.1038/s41598-025-11263-9
10. Joseph, J. (2025). Algorithmic bias in public health AI: a silent threat to equity in low-resource settings. Frontiers in Public Health, 13, 1643180. https://doi.org/10.3389/fpubh.2025.1643180
11. Kiseleva, A., Kotzinos, D., & De Hert, P. (2022). Transparency of AI in healthcare as a multilayered system of accountabilities: between legal requirements and technical limitations. Frontiers in artificial intelligence, 5, 879603. https://doi.org/10.3389/frai.2022.879603
12. Mackin, S., Major, V. J., Chunara, R., & Newton-Dame, R. (2025). Identifying and mitigating algorithmic bias in the safety net. npj Digital Medicine, 8(1), 335. https://doi.org/10.1038/s41746-025-01732-w
13. Maslej, N., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Kariuki, N., ... & Oak, S. (2025). Artificial intelligence index report 2025. arXiv preprint arXiv:2504.07139.
14. Mohammed, Z. K. (2025). Explainable AI in Health Care: Trust and Transparency in AI-Powered Medical Diagnosis. https://doi.org/10.5772/intechopen.1011279
15. Nazar, M., Alam, M. M., Yafi, E., & Su’ud, M. M. (2021). A systematic review of human– computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9, 153316-153348. https://doi.org/10.1109/ACCESS.2021.3127881
16. Nouis, S. C., Uren, V., & Jariwala, S. (2025). Evaluating accountability, transparency, and bias in AI-assisted healthcare decision-making: a qualitative study of healthcare professionals’ perspectives in the UK. BMC Medical Ethics, 26(1), 89. https://doi.org/10.1186/s12910-025- 01243-z
17. Pham, T. (2025). Ethical and legal considerations in healthcare AI: innovation and policy for safe and fair use. Royal Society Open Science, 12(5), 241873. https://doi.org/10.1098/rsos.241873
18. Provo, D. H., CCM, P. B., & Cahims, F. (n.d.). The ethical use of artificial intelligence in healthcare: Integrating case management, utilization review, and human collaboration. Retrieved December 11, 2026, from
19. Ratti, E., Morrison, M., & Jakab, I. (2025). Ethical and social considerations of applying artificial intelligence in healthcare a two-pronged scoping review. BMC Medical Ethics, 26(1), 68. https://doi.org/10.1186/s12910-025-01198-1
20. Singh, M. P., Keche, Y. N., & Keche, Y. (2025). Ethical Integration of Artificial Intelligence in Healthcare: Narrative Review of Global Challenges and Strategic Solutions. Cureus, 17(5). https://doi.org/10.7759/cureus.84804
21. Osnat, B. (2025). Patient perspectives on artificial intelligence in healthcare: A global scoping review of benefits, ethical concerns, and implementation strategies. International Journal of Medical Informatics, 106007. https://doi.org/10.1016/j.ijmedinf.2025.106007
22. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
23. Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS digital health, 4(4), e0000810. https://doi.org/10.1371/journal.pdig.0000810
24. Wells, B. J., Nguyen, H. M., McWilliams, A., Pallini, M., Bovi, A., Kuzma, A., ... & Isreal, M. (2025). A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare. NPJ digital medicine, 8(1), 514. https://doi.org/10.1038/s41746-025- 01900-y
25. World Health Organization. (2024). Ethics and governance of artificial intelligence for health: large multi-modal models. WHO guidance. World Health Organization.
26. Yu, S., Lee, S. S., & Hwang, H. (2024). The ethics of using artificial intelligence in medical research. Kosin Medical Journal, 39(4), 229-237. https://doi.org/10.7180/kmj.24.140