ArticleBMJ health & care informatics2025
Cracking the code: a scoping review to unite disciplines in tackling legal issues in health artificial intelligence.
Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges of artificial intelligence in public health: a systematic review on technological efficacy, ethical dilemmas, and governance pathways.Frontiers in public health · 2025Pooled it
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe 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.
methodsEmploying 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.
resultsCritical 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
conclusionDiffering 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.