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  9. Conflict Of Laws (incl. Private International Law)
  11. Cracking The Code: A Scoping Review To Unite Disciplines In Tackling Legal Issues In Health Artificial Intelligence

Cracking the code: a scoping review to unite disciplines in tackling legal issues in health artificial intelligence

Sophie Nunnelley1, Colleen M Flood2, Michael Da Silva3

  • 1Lincoln Alexander School of Law, Toronto Metropolitan University, Toronto, Ontario, Canada s.nunnelley@torontomu.ca.

BMJ Health & Care Informatics
|April 11, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Disciplinary differences in understanding and prioritizing legal issues in artificial intelligence (AI) for healthcare hinder progress. Inclusive dialogues are essential for effective law and policy reform in health AI.

Area of Science:

  • Legal implications of artificial intelligence in healthcare.
  • Interdisciplinary perspectives on health AI regulation.

Background:

  • Rapid integration of artificial intelligence (AI) in healthcare necessitates legal safeguards for safety, privacy, and non-discrimination.
  • Disciplinary differences in perspectives risk impeding effective legal reform for health AI.

Purpose of the Study:

  • To uncover convergences and divergences in disciplinary understanding, prioritization, and proposed solutions to legal issues in health AI.
  • To provide guidance for law and policymaking in the health AI domain.

Main Methods:

  • Scoping review methodology.
  • Searched multiple databases (MEDLINE, EMBASE, HeinOnline, etc.) for legal issue discussions from January 2012 to July 2021.
  • Analyzed 432 included studies, mapping legal concerns and solutions across various disciplines.
Keywords:
Artificial intelligenceDelivery of Health CareGlobal HealthHealth Equity

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Main Results:

  • Significant disciplinary differences exist in the frequency and nature of discussions on legal issues and solutions.
  • Computer science and engineering innovators showed minimal engagement with legal issues.
  • Law and medicine authors prioritized different legal issues and proposed distinct solutions.

Conclusions:

  • Divergent perspectives on law reform priorities and solutions jeopardize health AI development.
  • Inclusive, interdisciplinary dialogues are crucial for addressing risks and trade-offs in health AI law and policy.
  • Ensuring optimal law and policy reform requires collaborative approaches to health AI challenges.
Machine Learning