THE IMPACT OF ARTIFICIAL INTELLIGENCE ON HEALTHCARE: PRIVACY CHALLENGES AND CYBERSECURITY RISKS

Autores

  • Mirian Esquárcio Jabur Autor

DOI:

https://doi.org/10.63330/aurumpub.009-002

Palavras-chave:

Digital health, Artificial Intelligence, Information security, Data privacy, AI ethics, LGPD

Resumo

This article examines the advancements and challenges of digital transformation in healthcare, focusing on the adoption of artificial intelligence (AI) and its demonstrated benefits—such as reductions in diagnostic time and medical errors. It also addresses critical issues surrounding information security, protection of sensitive data, and ethics in the use of emerging technologies. Real‑world cases of cyber incidents are presented, the relevance of regulations such as Brazil’s General Data Protection Law (LGPD) is discussed, and guidelines are proposed to ensure responsible innovation that balances technological progress with the safeguarding of patients’ fundamental rights.

Downloads

Os dados de download ainda não estão disponíveis.

Referências

BRASIL. Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais (LGPD). Diário Oficial da União, Brasília, 2018.

CHECK POINT. Cyber Attack Trends: 2023 Mid-Year Report. Disponível em: https://blog.checkpoint.com. Acesso em: 22 mar. 2025.

CONSELHO FEDERAL DE MEDICINA. Resolução CFM nº 2.324, de 8 de dezembro de 2022. Uso de IA na prática médica. Brasília, 2022.

ESTEVA, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, v. 542, n. 7639, p. 115-118, 2017. DOI: 10.1038/nature21056.

ESTEVA, A. et al. A guide to deep learning in healthcare. Nature Medicine, 2019.

FBI. Data Breach Investigations Report, 2022. Disponível em: https://www.fbi.gov. Acesso em: 22 mar. 2025.

HOSPITAL SÍRIO-LIBANÊS. Relatório de Governança em IA 2022. São Paulo: HSL, 2023.

HOLZINGER, A. et al. Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2019.

ORGANIZAÇÃO MUNDIAL DA SAÚDE. Ethics and governance of artificial intelligence for health. Genebra: OMS, 2021.

PEW RESEARCH CENTER. Public Trust in Artificial Intelligence in Health, 2023. Disponível em: https://www.pewresearch.org. Acesso em: 22 mar. 2025.

RAJPURKAR, P. et al. Deep learning for detecting pneumonia from chest X-rays. arXiv preprint, arXiv:1711.05225, 2018. Disponível em: https://arxiv.org/abs/1711.05225. Acesso em: 21 jun. 2025.

SINGAPORE MINISTRY OF HEALTH. SingHealth cyber attack – official statement, 2018. Disponível em:

https://www.healthcareitnews.com/news/singhealth-cyberattack-raises-concerns-over-health-data-security. Acesso em: 21 jun. 2025.

SMITH, M. L. et al. Algorithmic accountability in healthcare. Journal of Medical Ethics, v. 49, n. 3, p. 172-178, 2023.

SOPHOS. The State of Ransomware in Healthcare 2023. Disponível em: https://www.sophos.com. Acesso em: 22 mar. 2025.

TOPOL, E. J. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York: Basic Books, 2019.

WIENS, J.; SHENOY, E. S. Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology. Clinical Infectious Diseases, 2018.

Publicado

2025-07-01

Como Citar

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON HEALTHCARE: PRIVACY CHALLENGES AND CYBERSECURITY RISKS. (2025). Aurum Editora, 13-38. https://doi.org/10.63330/aurumpub.009-002