Legal Aspects of the Use Artificial Intelligence in Telemedicine

Objective: the rapid expansion of the use of telemedicine in clinical practice and the increasing use of Artificial Intelligence has raised many privacy issues and concerns among legal scholars. Due to the sensitive nature of the data involved particular attention should be paid to the legal aspects of those systems. This article aimed to explore the legal implication of the use of Artificial Intelligence in the field of telemedicine, especially when continuous learning and automated decision-making systems are involved; in fact, providing personalized medicine through continuous learning systems may represent an additional risk. Particular attention is paid to vulnerable groups, such as children, the elderly, and severely ill patients, due to both the digital divide and the difficulty of expressing free consent.

Methods: comparative and formal legal methods allowed to analyze current regulation of the Artificial Intelligence and set up its correlations with the regulation on telemedicine, GDPR and others.

Results: legal implications of the use of Artificial Intelligence in telemedicine, especially when continuous learning and automated decision-making systems are involved were explored; author concluded that providing personalized medicine through continuous learning systems may represent an additional risk and offered the ways to minimize it. Author also focused on the issues of informed consent of vulnerable groups (children, elderly, severely ill patients).

Scientific novelty: existing risks and issues that are arising from the use of Artificial Intelligence in telemedicine with particular attention to continuous learning systems are explored.

Practical significance: results achieved in this paper can be used for lawmaking process in the sphere of use of Artificial Intelligence in telemedicine and as base for future research in this area as well as contribute to limited literature on the topic.

1. Ahmad, R. W. (2021). The role of blockchain technology in telehealth and telemedicine. International Journal of Medical Informatics, 148, 104399. https://doi.org/10.1016/j.ijmedinf.2021.104399

2. Almada, M. (2019). Human intervention in automated decision-making: Toward the construction of contestable systems. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law (pp. 2–11). https://doi.org/10.2139/ssrn.3264189

3. Botrugno, C. (2014). Un diritto per la telemedicina: analisi di un complesso normativo in formazione. Politica del Diritto, 4(45), 639–668. https://doi.org/10.1437/78949

4. Burrai, F., Gambella, M., & Scarpa, A. (2021). L’erogazione diprestazioni sanitarie in telemedicina. Giornale di Clinica Nefrologica e Dialisi, 33, 3–6.

5. Campagna, M. (2020). Linee guida per la Telemedicina: considerazioni alla luce dell’emergenza Covid-19. Corti Supreme e Salute, 3, 11–25.

6. Castagno, S., & Khalifa, M. (2020). Perceptions of artificial intelligence among healthcare staff: a qualitative survey study. Frontiers in artificial intelligence, 2(5), 84–92. https://doi.org/10.3389/frai.2020.578983

7. Davis, E. (2016). AI Amusements: The Tragic Tale of Tay the Chatbot. AI Matters, 2(4), 20–24. https://doi.org/10.1145/3008665.3008674

8. Edwards, L., & Veale, M. (2017). Slave to the algorithm: Why a right to an explanation is probably not the remedy you are looking for. Duke L. & Tech. Rev., 16, 18–26.

9. Floridi, L. (2022). capAI-A Procedure for Conducting Conformity Assessment of AI Systems in Line with the EU Artificial Intelligence Act. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4064091

10. Gallese, Ch. (2022). Suggestions for a revision of the European smart robot liability regime. In Proceedings of the 4th European Conference on the Impact of Artificial Intelligence and Robotics (ECIAIR 2022). https://doi.org/10.34190/eciair.4.1.851

11. Gioulekas, F. (2022). A Cybersecurity Culture Survey Targeting Healthcare Critical Infrastructures. Healthcare, 10, 327–333. https://doi.org/10.3390/healthcare10020327

12. Giunti, G. (2014). The Use of a Gamified Platform To Empower And Increase Patient Engagement in Diabetes Mellitus Adolescents. In American Medical Informatics Association Annual Symposium.

13. Jain, N., Gupta V., & Dass, P. (2022). Blockchain: A novel paradigm for secured data transmission in telemedicine. In Wearable Telemedicine Technology for the Healthcare Industry (pp. 33–52).

14. Kalra, A. (2020). Artificial Intelligence Ethics Canvas: A Tool for Ethical and Socially Responsible AI.

15. Koshiyama, A. S., Kazim, E., Treleaven, P. C., Rai, P., Szpruch, L., Pavey, G., Ahamat, G., Leutner, F., Goebel, R., Knight, A., Adams, J., Hitrova, C., Barnett, J., Nachev, P., Barber, D., Chamorro‐Premuzic, T., Klemmer, K., Gregorovic, M., Khan, S. A., & Lomas, E. (2021). Towards Algorithm Auditing: A Survey on Managing Legal, Ethical and Technological Risks of AI, ML and Associated Algorithms. Software Engineering eJournal. https://doi.org/10.2139/ssrn.3778998

16. LaBrie, R., & Steinke, G. (2019). Towards a framework for ethical audits of AI algorithms. In Twenty-fifth Americas Conference on Information Systems.

17. Lakkaraju, H. (2019). Faithful and customizable explanations of black box models. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 131–138). https://doi.org/10.1145/3306618.3314229

18. Ma, M., Shuqin, F., & Feng, D. (2020). Multi-user certificateless public key encryption with conjunctive keyword search for cloud-based telemedicine. Journal of Information Security and Applications, 55, 102652. https://doi.org/10.1016/j.jisa.2020.102652

19. Mantelero, A., & Esposito, S. (2021). An evidence-based methodology for human rights impact assessment (HRIA) in the development of AI data-intensive systems. Computer Law & Security Review, 41, 105561.

20. https://doi.org/10.1016/j.clsr.2021.105561

21. Marchant, G., & Lindor, R. (2012). The coming collision between autonomous vehicles and the liability system. Santa Clara Law Review, 52, 1321–1340.

22. Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and information technology, 6, 175–183. https://doi.org/10.1007/s10676-004-3422-1

23. Membrado, C. G. (2021). Telemedicina, ética y derecho en tiempos de COVID-19. Una mirada hacia el futuro. Revista Clinica Espanola, 221, 408–410. https://doi.org/10.1016/j.rce.2021.03.002

24. Mi, F. (2020). Generalized Class Incremental Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 240–241).

25. Mökander, J. (2022). Conformity assessments and post-market monitoring: a guide to the role of auditing in the proposed European AI regulation. Minds and Machines, 32, 241–268. https://doi.org/10.1007/s11023021-09577-4

26. Mökander, J., & Floridi, L. (2022). Operationalising AI governance through ethics-based auditing: an industry case study. AI and Ethics, 6, 1–18. https://doi.org/10.1007/s43681-022-00171-7

27. Oliveira, T. (2020). Bringing health care to the patient: An overview of the use of telemedicine in OECD countries. OECD, Directorate for Employment, Labour and Social Affairs, Health Committee.

28. Pacis, D., Mitch, M., Edwin, D. C., Subido, Jr., & Bugtai, N. (2018). Trends in telemedicine utilizing artificial intelligence. In AIP conference proceedings. AIP Publishing LLC.

29. Parisi, G. (2019). Continual lifelong learning with neural networks: A review, Neural Networks, 113, 54–71. https:// doi.org/10.1016/j.neunet.2019.01.012

30. Rodrigues, R. (2020). Legal and human rights issues of AI: Gaps, challenges and vulnerabilities. Journal of Responsible Technology, 4, 100005. https://doi.org/10.1016/j.jrt.2020.100005

31. Schatten, M., & Protrka, R. (2021). Conceptual Architecture of a Cognitive Agent for Telemedicine based on Gamification. In Central European Conference on Information and Intelligent Systems (pp. 3–10).

32. Scheetz, J. (2021). A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Scientific reports, 11.1, 1–10.

33. Shaw, S., Davis, L-J., & Doherty, M. (2022). Considering autistic patients in the era of telemedicine: the need for an adaptable, equitable, and compassionate approach, BJGP open 6.1.

34. Strehle, E. M., & Shabde, N. (2006). One hundred years of telemedicine: does this new technology have a place in paediatrics? Archives of disease in childhood, 91.12, 956–959. https://doi.org/10.1136/adc.2006.099622

35. Tigard, D. (2020). There is no techno-responsibility gap. Philosophy & Technology, 1–19.

36. Wang, R. (2022). Privacy-Preserving Federated Learning for Internet of Medical Things under Edge Computing. IEEE Journal of Biomedical and Health Informatics.

37. Wang, W. (2021). A privacy protection scheme for telemedicine diagnosis based on double blockchain. Journal of Information Security and Applications, 61, 102845. https://doi.org/10.1016/j.jisa.2021.102845

38. Yakar, D. (2021). Do People Favor Artificial Intelligence Over Physicians? A Survey Among the General Population and Their View on Artificial Intelligence in Medicine. Value in Health, 3, 12–23. https://doi.org/10.1016/j.jval.2021.09.004

39. Ye, J. (2020). The role of health technology and informatics in a global public health emergency: practices and implications from the COVID-19 pandemic. JMIR medical informatics, 8.7, e19866. https://doi.org/10.2196/19866