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

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%).