Cracking the Code: A Scoping Review to Unite Disciplines in Tackling Legal Issues in Health Artificial Intelligence
Bibliographic record
Abstract
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 risks impeding effective reform. This study uncovers convergences and divergences in disciplinary comprehension, prioritization, 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, prioritization, 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 prioritized different legal issues and proposed different solutions. Discussion and Conclusion Differing perspectives regarding law reform priorities and solutions jeopardize 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. KEY MESSAGES What is already known on this topic There has been no systematic examination of the multidisciplinary literature discussing legal challenges posed by health-AI. Prior efforts have addressed ethical concerns or limited subsets of legal issues or technologies, and therefore do not establish the comprehensive groundwork essential for fostering meaningful cross-disciplinary dialogue on health-AI regulation. What this study adds Our study uncovers a shared interdisciplinary apprehension regarding the effective regulation of health-AI. However, distinct stakeholders such as physicians, innovators, and legal scholars hold divergent perspectives on these issues and their relative significance. Notably, certain critical voices, such as within discussions around informed consent, are conspicuously absent, hindering the prospects of effective reform. How this study might affect research, practice, or policy The findings underscore the imperative for governments to facilitate inclusive dialogue and reconcile disparate disciplinary viewpoints. Effective regulation is pivotal in ensuring the safe and responsible deployment of health-AI for the public good. This study presents essential entry points for the much-needed discourse on this challenge facing governments around the world.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| grok | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| opus | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.058 | 0.044 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".