A Critical Review on Ethical and Privacy Challenges of AI-Powered Heal thcare Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P129Keywords:
Ethics of AI, Medical Privacy, Federated Learning, Differential Privacy, Governance, ResponsibilityAbstract
The emergence of AI-driven healthcare systems is deeming more and more on distributed analytics to train on sensitive clinical data without centralising raw-data. Federated learning (FL) has often been framed as an answer with privacy safeguards but healthcare implementations have revealed that FL transitions instead of eliminates ethical and privacy threats. An overview of privacy enhancement techniques in FL in health care presented in a review of Scopus described 216 records retrieved and a subsequent set of approximately forty records that were included, having passed the screening procedure, which reflected not only the fast growth of the evidence base but also its dispersal. [1]. Simultaneously, empirical research in imaging, clinical prediction, and internet-scale epidemiology demonstrates that privacy mechanisms (e.g., differential privacy) may decrease performance or increase inequities when data are non-IID, institutions do not have the same resources, or the external validity is low. These tensions have transformed privacy into a socio-technical governance issue: security measures, accountability, clinical safety and fairness should be considered as a whole and not as a side-note.
References
[1] X. Gu, F. Sabrina, Z. Fan, and S. Sohail, “A review of privacy enhancement methods for federated learning in healthcare systems,” Int. J. Environ. Res. Public Health, vol. 20, no. 15, Art. no. 6539, 2023, doi: 10.3390/ijerph20156539.
[2] M. Ali, F. Naeem, M. Tariq, and G. Kaddoum, “Federated learning for privacy preservation in smart healthcare systems: A comprehensive survey,” IEEE J. Biomed. Health Inform., vol. 27, no. 2, pp. 778–789, 2023, doi: 10.1109/JBHI.2022.3181823.
[3] J. Li, Y. Meng, L. Ma, S. Du, H. Zhu, Q. Pei, and X. Shen, “A federated learning based privacy-preserving smart healthcare system,” IEEE Trans. Ind. Informatics, vol. 18, no. 3, pp. 2021–2031, Mar. 2022, doi: 10.1109/TII.2021.3098010.
[4] M. Abaoud, M. A. Almuqrin, and M. F. Khan, “Advancing federated learning through novel mechanism for privacy preservation in healthcare applications,” IEEE Access, vol. 11, pp. 83562–83579, 2023, doi: 10.1109/ACCESS.2023.3301162.
[5] J. Xu, B. S. Glicksberg, C. Su, P. Walker, J. Bian, and F. Wang, “Federated learning for healthcare informatics,” J. Healthc. Inform. Res., vol. 5, no. 1, pp. 1–19, 2021, doi: 10.1007/s41666-020-00082-4.
[6] T. J. Loftus et al., “Federated learning for preserving data privacy in collaborative healthcare research,” Digit. Health, vol. 8, Art. no. 20552076221134455, 2022, doi: 10.1177/20552076221134455.
[7] O. Aouedi, A. Sacco, K. Piamrat, and G. Marchetto, “Handling privacy-sensitive medical data with federated learning: Challenges and future directions,” IEEE J. Biomed. Health Inform., vol. 27, no. 2, pp. 790–803, 2023, doi: 10.1109/JBHI.2022.3185673.
[8] R. S. Antunes, C. A. da Costa, A. Küderle, I. A. Yari, and B. Eskofier, “Federated learning for healthcare: Systematic review and architecture proposal,” ACM Trans. Intell. Syst. Technol., vol. 13, no. 4, Art. no. 54, pp. 54:1–54:23, 2022, doi: 10.1145/3501813.
[9] A. Sadilek et al., “Privacy-first health research with federated learning,” npj Digit. Med., vol. 4, no. 1, Art. no. 132, 2021, doi: 10.1038/s41746-021-00489-2.
[10] M. Adnan, S. Kalra, J. C. Cresswell, G. W. Taylor, and H. R. Tizhoosh, “Federated learning and differential privacy for medical image analysis,” Sci. Rep., vol. 12, no. 1, Art. no. 1953, 2022, doi: 10.1038/s41598-022-05539-7.
[11] T. U. Islam, R. Ghasemi, and N. Mohammed, “Privacy-preserving federated learning model for healthcare data,” in Proc. 2022 IEEE 12th Annu. Comput. Commun. Workshop Conf. (CCWC), Las Vegas, NV, USA, Jan. 2022, pp. 281–287, doi: 10.1109/CCWC54503.2022.9720752.
[12] D. C. Nguyen, Q.-V. Pham, P. N. Pathirana, M. Ding, A. Seneviratne, Z. Lin, O. A. Dobre, and W.-J. Hwang, “Federated learning for smart healthcare: A survey,” ACM Comput. Surv., vol. 55, no. 3, Art. no. 60, pp. 1–37, 2022, doi: 10.1145/3501296.
[13] A. Lakhan et al., “Federated-learning based privacy preservation and fraud-enabled blockchain IoMT system for healthcare,” IEEE J. Biomed. Health Inform., vol. 27, no. 2, pp. 664–672, 2023, doi: 10.1109/JBHI.2022.3165945.
[14] M. Butt et al., “A fog-based privacy-preserving federated learning system for smart healthcare applications,” Electronics, vol. 12, no. 19, Art. no. 4074, 2023, doi: 10.3390/electronics12194074.
[15] J. Woiceshyn and U. Daellenbach, “Evaluating inductive vs deductive research in management studies: Implications for authors, editors, and reviewers,” Qual. Res. Org. Manag.: Int. J., vol. 13, no. 2, pp. 183–195, 2018, doi: 10.1108/QROM-06-2017-1538.
[16] T. Azungah, “Qualitative research: Deductive and inductive approaches to data analysis,” Qual. Res. J., vol. 18, no. 4, pp. 383–400, 2018, doi: 10.1108/QRJ-D-18-00035.
[17] S. Murumkar, “Smart contract-driven consent management for personal data sharing,” Int. J. Artif. Intell., Data Sci., Mach. Learn., vol. 4, no. 2, pp. 135–141, 2023, doi: 10.63282/3050-9262.IJAIDSML-V4I2P115.
[18] C. Tayal, “Data quality assessment and cleaning framework for healthcare databases using Python,” Int. J. Artif. Intell., Data Sci., Mach. Learn., vol. 3, no. 4, pp. 107–112, 2022, doi: 10.63282/3050-9262.IJAIDSML-V3I4P112
