Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 10;32(1):e101112.
doi: 10.1136/bmjhci-2024-101112.

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

Sophie Nunnelley  1 , Colleen M Flood  2 , Michael Da Silva  3 , Tanya Horsley  4 , Sarathy Kanathasan  5 , Bryan Thomas  2 , Emily Ann Da Silva  6 , Valentina Ly  6 , Ryan C Daniel  7 , Mohsen Sheikh Hassani  8 , Devin Singh  9
Affiliations

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

Sophie Nunnelley et al. BMJ Health Care Inform. .

Abstract

Objectives: The rapid integration of artificial intelligence (AI) in healthcare requires robust legal safeguards to ensure safety, privacy and non-discrimination, crucial for maintaining trust. Yet, unaddressed differences in disciplinary perspectives and priorities risk impeding effective reform. This study uncovers convergences and divergences in disciplinary comprehension, prioritisation and proposed solutions to legal issues with health-AI, providing law and policymaking guidance.

Methods: Employing a scoping review methodology, we searched MEDLINE (Ovid), EMBASE (Ovid), HeinOnline Law Journal Library, Index to Foreign Legal Periodicals (HeinOnline), Index to Legal Periodicals and Books (EBSCOhost), Web of Science (Core Collection), Scopus and IEEE Xplore, identifying legal issue discussions published, in English or French, from January 2012 to July 2021. Of 18 168 screened studies, 432 were included for data extraction and analysis. We mapped the legal concerns and solutions discussed by authors in medicine, law, nursing, pharmacy, other healthcare professions, public health, computer science and engineering, revealing where they agree and disagree in their understanding, prioritisation and response to legal concerns.

Results: Critical disciplinary differences were evident in both the frequency and nature of discussions of legal issues and potential solutions. Notably, innovators in computer science and engineering exhibited minimal engagement with legal issues. Authors in law and medicine frequently contributed but prioritised different legal issues and proposed different solutions.

Discussion and conclusion: Differing perspectives regarding law reform priorities and solutions jeopardise the progress of health AI development. We need inclusive, interdisciplinary dialogues concerning the risks and trade-offs associated with various solutions to ensure optimal law and policy reform.

Keywords: Artificial intelligence; Delivery of Health Care; Global Health; Health Equity; Machine Learning.

PubMed Disclaimer

Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses diagram.
Figure 2
Figure 2. Growth in discussions of legal issues with health artificial intelligence (AI) from 2012 to 2021*. *2021 values prorated. There has been exponential growth in the multidisciplinary literature discussing legal issues with AI, with especially strong growth beginning in 2018. Top-ranking issues include efficiency of regulation, privacy and safety/quality.
Figure 3
Figure 3. Geographical distribution of publications discussing legal issues with health artificial intelligence (AI) (2012–2021). Authors with English or French-language publications discussing legal issues with health AI are predominantly located in the USA, followed by the UK, Canada and Australia. Many countries, especially in the Global South, are not represented in this literature.
Figure 4
Figure 4. Disciplinary distributions of discussions of legal issues with health artificial intelligence (AI) (2012–2021). Writers in medicine produced the most discussions of legal issues (36%), followed by law (28%); other (11%); unknown (no discipline indicated, 10%); engineering (4%); computer science (4%); other health professions (dentistry, nutrition, etc, 1%); pharmacy (1%); and nursing (less than 1%). Disciplines were united in writing most frequently about the efficiency of regulation but went on to prioritise different legal issues.
Figure 5
Figure 5. Disciplinary distribution of references to solutions to legal issues with health artificial intelligence (AI) (2012–2021). Writers in medicine and law produced the most discussions (by far) of possible solutions to legal problems with health AI. However, they proposed different solutions. Legal writers were most likely to propose new legislation, followed by reform of existing law. Writers in medicine were most likely to discuss calls (by industry, clinicians or others) for voluntarily improving standards, followed by new legislation. Writers in medicine were also more likely to call for mandatory training as a response to legal problems.
See this image and copyright information in PMC

References

    1. Rajpurkar P, Chen E, Banerjee O, et al. AI in health and medicine. Nat Med. 2022;28:31–8. doi: 10.1038/s41591-021-01614-0. - DOI - PubMed
    1. Geneva: World Health Organization; 2023. Regulatory considerations on artificial intelligence for health, licence: CV BY-NC-SA 3.0 IGO.
    1. American medical association principles for augmented intelligence development, deployment, and use. 2023. [12-Dec-2023]. https://www.ama-assn.org/press-center/press-releases/ama-issues-new-prin... Available. Accessed.
    1. Royal College of Physicians and Surgeons of Canada Task force report on artificial intelligence and emerging digital technologies. 2020. [26-Jun-2023]. https://cdn.dal.ca/content/dam/dalhousie/pdf/faculty/medicine/department... Available. Accessed.
    1. Civaner MM, Uncu Y, Bulut F, et al. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022;22:772. doi: 10.1186/s12909-022-03852-3. - DOI - PMC - PubMed

Publication types

MeSH terms

LinkOut - more resources