LEGAL ASPECTS OF USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN HEALTHCARE
Keywords:
artificial intelligence, medical law, GDPR, confidentialityAbstract
This article examines the theoretical and legal aspects of using artificial intelligence technologies in healthcare system. Focusing on key elements such as the current applications of AI in medical practice, its potential prospects and emerging challenges, the application of international standards and legal accountability, the article analyzes various studies demonstrating the diverse uses of AI in healthcare, highlighting both the benefits and challenges associated with its implementation. The article also emphasizes the legal basis for AI decision-making, algorithmic bias, transparency and the need for human oversight.
References
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE journal of biomedical and health informatics, 22(5), P. 1589
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L.H. and Aerts, H.J. (2018) Artificial Intelligence in Radiology. Nature Reviews Cancer, 18, P. 500-510
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., & Coiera, E. (2018). Conversational agents in healthcare: a systematic review. Journal of the American Medical Informatics Association : JAMIA, 25(9), P. 1248–1258
Fleming, J., & Zegwaard, K. E. (2018). Methodologies, Methods and Ethical Considerations for Conducting Research in Work-Integrated Learning. International Journal of Work-Integrated Learning, Special Issue, 19, P. 205-213
Lundervold, A. S., & Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift fur medizinische Physik, 29(2), P. 102–127
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. The New England journal of medicine, 380(14), P.1347–1358
Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015). The emerging field of mobile health. Science translational medicine, 7(283), P. 188
De Fauw, J., Ledsam, J.R., Romera-Paredes, B. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. (2018). Nat Med 24, P. 1342–1350
Somashekhar, S. P., Sepúlveda, M. J., Puglielli, S., Norden, A. D., Shortliffe, E. H., Rohit Kumar, C., Rauthan, A., Arun Kumar, N., Patil, P., Rhee, K., & Ramya, Y. (2018). Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of oncology: official journal of the European Society for Medical Oncology, 29(2), P. 420
Press Release, August 11. 2020. Canon Medical Systems USA, Inc. Partners With Zebra Medical Vision to Further Expand AI Offerings. Canon Medical Systems. URL: https://us.medical.canon/news/press-releases/2020/08/11/3390/>, Date of access: 18.10.2023
Huusko, J., Kinnunen, U. M., & Saranto, K. (2023). Medical device regulation (MDR) in health technology enterprises - perspectives of managers and regulatory professionals. BMC health services research, 23(1), P. 302
Chouldechova, A., & Roth, A. (2018). The frontiers of fairness in machine learning. P.2-5
Wexler, David B., The DNA of Therapeutic Jurisprudence (November 14, 2020). Arizona Legal Studies Discussion Paper No. 20-43, The Methodology and Practice of Therapeutic Jurisprudence (2019) Carolina Academic Press. Edited by Nigel Stobbs, Lorana Bartels, and Michel Vols., P.5
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). P.2-3
Goodman, B., & Flaxman, S. (2017). European Union Regulations on Algorithmic Decision-Making and a “Right to Explanation”. AI Magazine, 38(3), P. 50-57
Selbst, Andrew D. and Barocas, Solon, The Intuitive Appeal of Explainable Machines. 87 Fordham Law Review 1085 (2018), P.38
Adadi and M. Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," in IEEE Access, vol. 6, pp. 52138-52160, 2018, P.55
Roff, Heather M. and Moyes, Richard. “Meaningful Human Control, Artificial Intelligence and Autonomous Weapons.” Briefing paper prepared for the Informal Meeting of Ex- perts on Lethal Autonomous Weapons Systems, UN Con- vention on Certain Conventional Weapons, April 2016, P.2
European Economic and Social Committee plenary session of 31 May and 1 June 2017, Opinion of the European Economic and Social Committee on ‘Artificial intelligence — The consequences of artificial intelligence on the (digital) single market, production, consumption, employmentandsociety’(own-initiativeopinion), 2017/C 288/01, P.12
Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices, The U.S. Food and Drug Administration (FDA), 2023, URL: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices, Date of access: 18.102023
Ross & Swetlitz (2017) Ross C, Swetlitz I. IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. 2017. P.2
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.