From leadership research to district action: Artificial Intelligence Governance in Health Systems: Systematic Review of Frameworks and Integrative Model Proposal

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: review-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

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.

Study methods

This systematic review was conducted and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) Literature Search Extension statement (Checklist 1) [3940]. The full description of methods is provided in the protocol registered on the Open Science Framework prior to data collection [41]. No modifications or additional information were made to the protocol following its registration (PRISMA item 24c). In addition, the following PRISMA checklist items were considered not applicable to this review: 10b, 12, 13e, 13f, 15, 19, 20b, 20d, and 22 (Checklist 1). Given the qualitative nature of this systematic review, these items were not relevant to the methodological approach adopted in this study. Ethical approval was not required for this systematic review.

Limitations

To the best of our knowledge, this systematic review is the first to synthesize existing AI governance frameworks across 4 governance levels while simultaneously assessing their quality. Yet, this study has certain limitations. First, although our search strategy was comprehensive and extended to gray literature, it remains possible that frameworks in progress or those disseminated in other languages were not identified. Regarding language restrictions, we acknowledged that relevant frameworks may exist in other languages, particularly in countries that are advanced in AI development (eg, China and Germany). However, we did not conduct literature searches in these languages as our team does not possess the linguistic expertise required to analyze documents in these languages. Relying on automated translation tools was not considered an appropriate option, as these tools pose a substantial risk of misinterpreting concepts and terms whose meaning depends heavily on context. Consequently, our model carries a Western bias, as the underlying evidence is primarily sourced from the Global North. This disparity underscores a critical need for future research to investigate AI governance within contexts of the Global South.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Hassane Alami; Renata Pozelli Sabio; Elsury Johanna Pérez; Marie-Pierre Gagnon; Lyse Langlois; Jean-Louis Denis; Kathy Malas; Lysanne Rivard; Mathilde Savoldelli; Mohamed Ali Ag Ahmed; Jean-Paul Fortin. Artificial Intelligence Governance in Health Systems: Systematic Review of Frameworks and Integrative Model Proposal. Journal of Medical Internet Research (2026-06-08). https://doi.org/10.2196/87448

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.