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Cited by 9Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIDigitalization, Law, and Regulation

Artificial Intelligence Governance in Health Systems: Systematic Review of Frameworks and Integrative Model Proposal

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Alami, Hassane, Pozelli Sabio, Renata, Pérez, Elsury Johanna, Gagnon, Marie-Pierre, Langlois, Lyse, Denis, Jean-Louis, Malas, Kathy, Rivard, Lysanne +3 more·2026·Journal of Medical Internet Research
DOI 10.2196/87448PMC PMC13245845
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Abstract

Background: Several artificial intelligence (AI) governance frameworks have emerged to help health systems (HS) address AI-related risks. However, most fail to capture the multidimensional and evolving nature of real-world governance. Objective: This systematic review aimed to synthesize existing AI governance frameworks for HS and to propose an integrative AI governance model identifying key components to guide AI-related policy, practice, and research in HS. Methods: A comprehensive search was conducted in 8 academic databases (PubMed, MEDLINE, Embase, ACM Digital Library, Web of Science, Scopus, Social Sciences Abstracts, and PsycINFO), gray literature databases, and international organization web portals in October 2024 (updates: July 2025 and March 2026) and limited to studies published from November 2014 to March 2026 in English, French, Spanish, or Portuguese. Eligible documents included peer-reviewed articles and reports proposing AI governance frameworks for HS. Two reviewers independently selected the frameworks, assessed their quality using the Appraisal of Guidelines for Research and Evaluation for Health Systems, and extracted data. Results were synthesized using thematic analysis. Results: The research retrieved 10,175 records, among which 19 AI governance frameworks were identified. Most were published between 2022 and 2024 (n=13, 68%), half (n=10, 53%) were developed by authors based in North America, and only one-third (n=6, 32%) were derived from primary studies. The frameworks focused on 4 levels of AI governance: international (n=3, 16%), national (n=5, 26%), local (n=3, 16%), and organizational (n=8, 42%). All of them underline the crucial role of multidisciplinary bodies in the structure of AI governance in HS. Six key AI governance processes in HS emerged as critical: (1) need and/or problem identification (n=14, 74%), (2) data governance (n=17, 89%), (3) risk assessment and management (n=17, 89%), (4) validation and/or evaluation (n=18, 95%), (5) maintenance and monitoring (n=16, 84%), and (6) integration (n=9, 47%). Additionally, 4 pivotal relational mechanisms were identified: (1) ethical principles and/or values (n=17, 89%), (2) education and training (n=14, 74%), (3) communication (n=12, 63%), and (4) standards and regulations (n=13, 68%). Conclusions: Our study provides a comprehensive synthesis of existing AI governance frameworks for HS across 4 levels (local, regional, national, and international), underpinned by a quality assessment of the 19 identified frameworks. It differs from existing studies that concentrate on specific dimensions or settings by contributing an integrative AI governance model for HS comprising 2 dimensions and 4 relational mechanisms across the 4 levels, explicitly modeling their interactions. Future research should test and operationalize the proposed model to enhance its practical applicability. Strengthening the methodological rigor of AI governance frameworks will be essential for the responsible integration of AI in HS. As the framework is primarily grounded in Global North and English-language literature, validation in other contexts is warranted.

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  • This reduces rework and supports future submissions when similar calls reopen.

    “This reduces rework and supports future submissions when similar calls reopen [3].”

    From A Workflow for Grant Discovery and Proposal Development Using Large Language Models: Development and Formative Evaluation (2026) · cites this paper as [3]

    The Methods (Eligibility Criteria) describe how AI-governance documents were selected, and the Results (Governance Processes) discuss monitoring and reviewer feedback. Neither addresses reducing rework or preparing future submissions when similar calls reopen. Searches for that phrasing and for funding applications or reusable submissions found no supporting passage; page numbers were not available in our copy. (LLM, conf=0.76)

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Authors
Alami, H., Pozelli Sabio, R., Pérez, E. J., Gagnon, M.-P., Langlois, L., Denis, J.-L., Malas, K., Rivard, L., Savoldelli, M., Ag Ahmed, M. A., & Fortin, J.-P.
Affiliations
  • Institut de Valorisation des Données (CA)
  • Centre de recherche en santé publique (CA)
  • Université Laval (CA)
  • University of Toronto (CA)
  • Centres Intégré Universitaires de Santé et de Services Sociaux (CA)
Corresponding author
Alami, Hassane
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2026-06-08
Volume
28
Pages
e87448-e87448
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/87448
PMC
PMC13245845
OpenAlex
W4416095593
Cited by
9
References
64
Citations by year
2026: 9
FWCI
5.52
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (acceptedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
AI governance, health care system, AI governance framework, data governance, risk assessment and management, artificial intelligence integration, ethical principles, AI policy, AI regulation, artificial intelligence in healthcare
MeSH terms
Artificial Intelligence, Delivery of Health Care, Humans

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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