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The Legal and Ethical Implications of Artificial Intelligence in Healthcare

 

Brief Description

Artificial Intelligence (AI) is changing how doctors care for patients. It helps find illnesses faster and makes treatments just for each person. But using AI in hospitals brings up big questions about laws and rules.

This topic is important for IT policy and law because we need to protect people’s health information. We also have to decide who is responsible if AI makes a mistake. Creating good policies and laws will keep patients safe and let new technology grow.

Experts say we need new rules for AI in healthcare (Jiang et al., 2017; Price & Cohen, 2019). For example, if an AI gives the wrong diagnosis, who is to blame (Yu & Kohane, 2019)? Also, using patient data to teach AI raises questions about permission and following laws like HIPAA and GDPR (Vayena et al., 2018).

Key Ideas to Explore

  • Keeping Data Private and Safe
    • How AI collects and uses patient information (Shaban-Nejad et al., 2018).
    • Following rules like HIPAA and GDPR to protect data (Wachter, 2020).
    • Getting permission from patients and hiding their identities (Elliott et al., 2019).
  • Who Is Responsible
    • What happens if AI makes a mistake in healthcare (Gerke et al., 2020).
    • Real stories where AI caused problems (Knight, 2017).
    • Ideas for laws to make sure someone is accountable.
  • Laws and Rules for AI
    • Current laws about AI in healthcare and where they might be lacking (Martinez-Martin & Kreitmair, 2018).
    • How we can improve laws for new AI technologies (Topol, 2019).
    • The role of groups like the FDA in watching over AI in healthcare (Benjamens et al., 2020).
  • Fairness and Doing the Right Thing
    • Making sure AI is fair and doesn’t treat anyone unfairly (Char et al., 2018).
    • Making AI decisions clear so people understand them (Samek et al., 2017).
    • Balancing new technology with keeping patients safe.
  • Future Directions
    • How AI might change in the future and what new laws we might need (He et al., 2019).
    • Working with other countries to set good rules for AI in healthcare (Floridi et al., 2018).
    • Bringing together experts in law, technology, and healthcare to solve these issues.

 

References

Benjamens, S., Dhunnoo, P., & Mesko, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. NPJ Digital Medicine, 3, Article 118. https://doi.org/10.1038/s41746-020-00324-0

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—Addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229

Elliott, C., Ross, E., & Barros, T. (2019). Patient consent for data use in health research: A literature review. International Journal of Medical Informatics, 126, 1–13. https://doi.org/10.1016/j.ijmedinf.2019.03.004

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Gerke, S., Minssen, T., & Cohen, I. G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Medicine, 113, 101–103. https://doi.org/10.1016/j.artmed.2020.101103

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101

Knight, W. (2017, April 11). The dark secret at the heart of AI. MIT Technology Review. https://www.technologyreview.com/2017/04/11/5113/the-dark-secret-at-the-heart-of-ai/

Martinez-Martin, N., & Kreitmair, K. (2018). Ethical issues for direct-to-consumer digital psychotherapy apps: Addressing accountability, data protection, and consent. JMIR Mental Health, 5(2), Article e32. https://doi.org/10.2196/mental.9423

Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43. https://doi.org/10.1038/s41591-018-0272-7

Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv Preprint arXiv:1708.08296. https://doi.org/10.48550/arXiv.1708.08296

Shaban-Nejad, A., Michalowski, M., & Buckeridge, D. L. (2018). Health intelligence: How artificial intelligence transforms population and personalized health. NPJ Digital Medicine, 1, Article 53. https://doi.org/10.1038/s41746-018-0058-9

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLOS Medicine, 15(11), Article e1002689. https://doi.org/10.1371/journal.pmed.1002689

Wachter, S. (2020). Affinity profiling and discrimination by association in online behavioral advertising. Berkeley Technology Law Journal, 35(2), 367–435. https://doi.org/10.15779/Z38BR8MF9G

Yu, K. H., & Kohane, I. S. (2019). Framing the challenges of artificial intelligence in medicine. BMJ Quality & Safety, 28(3), 238–241. https://doi.org/10.1136/bmjqs-2018-008551