The regulation of artificial intelligence for health in Brazil begins with the General Personal Data Protection Law
DOI:
https://doi.org/10.11606/s1518-8787.2022056004461Keywords:
Health Services Research, Artificial Intelligence, legislation & jurisprudence, Machine Learning, Health LawAbstract
Artificial intelligence develops rapidly and health is one of the areas where new technologies in this field are most promising. The use of artificial intelligence can modify the way health care and self-care are provided, besides influencing the organization of health systems. Therefore, the regulation of artificial intelligence in healthcare is an emerging and essential topic. Specific laws and regulations are being developed around the world. In Brazil, the starting point of this regulation is the Lei Geral de Proteção de Dados Pessoais (LGPD – General Personal Data Protection Law), which recognizes the right to explanation and review of automated decisions. Discussing the scope of this right is needed, considering the necessary instrumentalization of transparency in the use of artificial intelligence for health and the currently existing limits, such as the black-box system inherent to algorithms and the trade-off between explainability and accuracy of automated systems.
References
Obermeyer Z, Emanuel EJ. Predicting the future: big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-9. https://doi.org/10.1056/NEJMp1606181
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44-56. https://doi.org/10.1038/s41591-018-0300-7
World Health Organization. mHealth: use of appropriate digital technologies for public health: report by the director-general. In: 71. WHO Assembly; 2018 Mar 26; Geneva, Switzerland. Geneva (CH): WHO; 2018 [citado 1 fev 2022]. Disponível em: https://apps.who.int/gb/ebwha/pdf_files/WHA71/A71_20-en.pdf
Richman B. Health regulation for the digital age: correcting the mismatch. N Engl J Med. 2018;379(18):1694-5. https://doi.org/10.1056/NEJMp1806848
World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva (CH): WHO; 2021 [citado 1 fev 2022]. Disponível em: https://www.who.int/publications/i/item/9789240029200
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58. https://doi.org/10.1056/NEJMra1814259
Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1:389-99. https://doi.org/10.1038/s42256-019-0088-2
Watson DS, Krutzina J, Bruce IN, Griffiths CE, McInnes IB, Barnes MR et al. Clinical applications of machine learning algorithms: beyond the black box. BMJ. 2019;364:l886. https://doi.org/10.1136/bmj.l886
Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLoS Med. 2018;15(11):e1002689. https://doi.org/10.1371/journal.pmed.1002689
Kaminski ME. The right to explanation, explained. Berkeley Technol Law J. 2019;34(1):189-218. https://doi.org/10.15779/Z38TD9N83H
Price WN II. Medical malpractice and black-box medicine. In: Cohen IG, Lynch HF, Vayena E, Gasser U, editors. Big data, health law, and Bioethics. Cambridge (UK): Cambridge University Press; 2018. Chapter 20; p. 295-306. https://doi.org/10.1017/9781108147972.027
Wachter S, Mittelstadt B, Floridi L. Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. Int Data Priv Law. 2017;7(2):76-99. https://doi.org/10.1093/idpl/ipx005
Selbst AD, Powles J. Meaningful information and the right to explanation. Int Data Priv Law. 2017;7(4):233-42. https://doi.org/10.1093/idpl/ipx022
Khorsandi SE, Hardgrave HJ, Osborn T, Klutts G, Nigh J, Spencer-Cole RT, et al. Artificial intelligence in liver transplantation. Transplant Proc. 2021;53(10):2939-44. https://doi.org/10.1016/j.transproceed.2021.09.045
Monteiro R, Machado C, Silva L. The right to explanation in Brazilian data protection law. RIDDN [Internet]. 2021[citado 01 fev 2022];7(1):119-136. Disponível em: https://ojs.imodev.org/?journal=RIDDN&page=article&op=view&path%5B%5D=406
Burrell J. How the machine ‘thinks’: understanding opacity in machine learning algorithms. Big Data Soc. 2016;3(1):1-12. https://doi.org/10.1177/2053951715622512
Organisation for Economic Co-Operation and Development. Artificial Intelligence in society. Paris (FR): Éditions OCDE; 2019.
Doshi-Velez F, Kortz M, Budish R, Bavitz C, Gershman S, O'Brien D, et al. Accountability of AI under the law: the role of explanation. arXiv:1711.01134v3 [Preprint]. 2017 [citado 3 nov 2017; revisado 20 dez 2019]. Disponível em: https://arxiv.org/abs/1711.01134v3
Lipton ZC. The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue. 2018;16(3):31-57. https://doi.org/10.1145/3236386.3241340
Holzinger A, Biemann C, Pattichis CS, Kell DB. What do we need to build explainable AI systems for the medical domain? arXiv:1712.09923v1 [Preprint]. 2017 [citado 28 dez 2017]. Disponível em: https://arxiv.org/abs/1712.09923v1
London AJ. Artificial Intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep. 2019;49(1):15-21. https://doi.org/10.1002/hast.973
Ghassemi M, Oakden-Rayner Luke, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745-50. https://doi.org/10.1016/S2589-7500(21)00208-9
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