ArticleJAMIA open2026
The governance of health data in the AI era: a scoping review and computational topic model of the global research landscape.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of artificial intelligence (AI) into health care is critically dependent on vast quantities of patient data, igniting an urgent global debate on data ownership, privacy, and governance. While numerous perspectives exist, the empirical structure and evolution of this scholarly discourse remain uncharacterized. We aimed to systematically map the conceptual landscape of research on AI and health data governance to identify its core themes, temporal trends, and key focus areas. Methods: We conducted a scoping review according to PRISMA guidelines, searching PubMed, Scopus, and Web of Science for peer-reviewed articles published between January 1, 2018, and May 31, 2025. We performed a descriptive analysis of publication trends. Using Latent Dirichlet Allocation (LDA), we applied computational topic modelling to the abstracts, which serve as concise summaries of each article's core contributions, to identify latent thematic structures. Topic trends were analyzed using linear regression. Findings: Forty-three articles met the inclusion criteria. The volume of publications has increased substantially since 2018. Our LDA analysis identified five distinct research topics: (1) AI Applications & Ownership, (2) AI Models & Data Privacy, (3) Data Sharing Platforms & Technology, (4) Ethical & Legal Concerns, and (5) AI Development & Implementation. Over the study period, research on Ethical & Legal Concerns showed a statistically significant increasing trend in prevalence (slope = 0.023, Interpretation: The scholarly discourse on AI and health data has matured, shifting from foundational questions of technical implementation towards a dominant focus on complex ethical and legal challenges. This data-driven evidence signals an urgent need for clinical leaders and policymakers to move beyond theoretical discussions and implement robust, practical governance frameworks. Failure to address this governance gap risks impeding trustworthy AI innovation and eroding public trust, thereby limiting the potential of AI to improve patient outcomes equitably.
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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.