Challenges of Protecting Patient Health Data in Smart Healthcare
Akhlāq-i zīstī i.e., Bioethics Journal,
Vol. 15 No. 40 (1404),
14 July 2025
,
Page 1-15
https://doi.org/10.22037/bioeth.v15i40.48354
Abstract
Background and Aim: The processing of individuals' data by artificial intelligence in the health sector is closely linked to the necessity of preserving their privacy. Accordingly, the aim of the present article is to examine, on one hand, ethical challenges such as informed consent, inequality and discrimination, and lack of transparency and explain ability, and on the other hand, legal challenges such as the legal personality of therapeutic robots, unauthorized access and breach of patient data confidentiality, and secondary use of data, in the realm of protecting patients' personal health data.
Methods: This article, using a descriptive-analytical method and library resources, examines the ethical and legal challenges of protecting patient health data in smart healthcare.
Ethical Considerations: In all stages of writing this research, the originality of texts, honesty, and trustworthiness have been observed.
Results: Protecting patient health data is an essential matter in the field of smart healthcare. Based on this, the European Union has made the first efforts to enact protective laws in the form of data protection regulations in 2016. However, in Iranian law, the issue of data protection has not been specifically addressed, and it seems that enacting data protection regulations, especially health-centric data regulations, as soon as possible, is a necessity.
Conclusion: Since artificial intelligence is an emerging phenomenon in various domains such as legal and medical sciences, different countries are striving to enact relevant regulations. The complexity, technicality, and specialized nature of artificial intelligence have created numerous ethical and legal challenges, such as the issue of the possibility of recognizing an independent legal personality for therapeutic robots, the potential breach of patient health data by medical centers, and the use of discriminatory data in patient treatment, matters that had not been raised in legal science before.
- Privacy
- Therapeutic Robot
- Data Protection
- Medical Law
How to Cite
References
1. Sharafoddini A, Dubin JA, Lee J. Patient similarity in prediction models based on health data: a scoping review. JMIR medical informatics. 2017; 5(1): e6730.
2. Ansari B. Mass Communication Law.1th Ed.Tehran: Samt Publications; 2022. [Persian]
3. Lupton M, Australia GC. Can Patient Information Held by an AI Robot Be Protected by the Duty of Confidentiality?. International Journal of Medical Science and Health Research. 2020; 4(4): 41-55.
4. Manheim K, Kaplan L. Artificial Intelligence: Risks to Privacy and Democracy. Yale Journal of Law and Technology. 2019; 37(21): 151-160.
5. Waldoch K. Informed Consent for the Use of AI in the Process of Providing Medical Services: Review of European and Comparative Law. 2024; 57(2): 121-134
6. Iserson KV. Informed consent for artificial intelligence in emergency medicine: A practical guide. The American Journal of Emergency Medicine. 2024; 76: 225-230.
7. Cohen IG. Informed Consent and Medical Artificial Intelligence: What to Tell the Patient?. The Georgetown Law Journal. 2020; 108(20): 1425–1469
8. Caliskan A, Bryson JJ, Narayanan A. Semantics derived automatically from language corpora contain human-like biases. Science. 2017; 356(6334): 183-186.
9. Wojcik MA. Algorithmic discrimination in health care: an EU law perspective. Health and Human Rights. 2022; 24(1): 93-103.
10. Schönberger D. Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. International Journal of Law and Information Technology. 2019; 27(2): 171-203.
11. Loyola-Gonzalez O. Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view. IEEE access. 2019; 7: 154096-113.
12. Voigt P, Von dem Bussche A. The eu general data protection regulation (gdpr). A practical guide, 1st ed., Cham: Springer International Publishing; 2017.
13. Kiseleva A, Kotzinos D, De Hert P. Transparency of AI in healthcare as a multilayered system of accountabilities: between legal requirements and technical limitations. Frontiers in artificial intelligence. 2022; 5: 879603.
14. Florina M. Liability Issues Concerning Self-Driving Vehicles: EJRR Special Issue on the Man and the Machine. 2016; 7(2): 335-341.
15. Shahbazinia M, Zolghadr MJ. Recognizing Artificial Intelligence (AI) As A Legal Person: Providing A Policy Proposal to The Iranian Legislator. Journal of Science and Technology Policy. 2024; 17(3): 41-53.[Persian]
16. Hekmatnia M, Mohammadi M, Vaseghi M. civil Liability for damages caused by robots based on autonomous artificial intelligence. Islamic Law. 2019; 16(60): 231-258. [Persian]
17. Staszkiewicz P, Horobiowski J, Szelągowska A, Strzelecka AM. Artificial intelligence legal personality and accountability: auditors’ accounts of capabilities and challenges for instrument boundary. Meditari Accountancy Research. 2024; 32(7): 120-146.
18. Parliament EU. Civil Law Rules on Robotics: European Parliament Resolution of 16 February 2017 with Recommendations to the Commission on Civil Law Rules on Robotics (2015/2103 (INL)). Official Journal of the European Union https://www. europarl. europa.eu/doceo/document/TA-8-2017-0051_EN. html (accessed 9/10/2024). 2017.
19. World Economic Forum, ‘Healthcare pays the highest price of any sector for cyberattacks – that’s why cyber resilience is key’, 1 February 2024 https://www.weforum.org/agenda. accessed 19 April 2024.
20. Karimi A, Rahimipour I, Hassani M. Telemedicine crimes resulted from electronic health. Medical Law Journal. 2010; 4(14): 47-69. [Persian]
21. Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS. Adversarial attacks on medical machine learning. Science. 2019; 363(6433): 1287-1289.
22. McLeod A, Dolezel D. Cyber-analytics: Modeling factors associated with healthcare data breaches. Decision Support Systems. 2018; 108: 57-68.
23. Health data breach: Dedalus Biologie fined 1.5 million euros.Web site. https://www.edpb.europa.eu/ news/national-news.Updated 15 April 2022. Accessed 4 May 2022.
24. Nguyen T. The Ethical Governance of Artificial Intelligence and Machine Learning in Healthcare. 1th Ed, New York: Ethics International Press Limited; 2023.
25. Shah SM, Khan RA. Secondary use of electronic health record: Opportunities and challenges. IEEE access. 2020; 8: 136947-65.
26. Kerasidou A, Kerasidou C. Data-driven research and healthcare: public trust, data governance and the NHS. BMC medical ethics. 2023; 24(1): 51.
27. Ho CH. Secondary use of health data for medical AI: A cross-regional examination of Taiwan and the EU. Asian Bioethics Review. 2024; 16(3): 407-422.
28. Dehghanpour S, Navid R. Investigating the Threats to Privacy and the Legal Requirements for Protecting It in the Use of Self-Driving Vehicles: Quarterly Journal of Private Law Studies. 2022; 51(4): 695-715. [Persian]
29. WHO guidance on Artificial Intelligence to improve healthcare, mitigate risks worldwide. UN News, Web site: https://news.un.org/en/story/2021/ 06/1094902. 28 June 2021, accessed 19 April 2024
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