JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is rapidly transforming health systems, expanding from diagnostic imaging and predictive analytics to large language model–enabled clinical decision support. However, significant governance challenges persist, including algorithmic bias, privacy risks, limited transparency, and inequities in access. Despite the proliferation of national AI strategies, global governance remains fragmented, and systematic evidence on how national policies address ethical, regulatory, and implementation requirements is limited. No comprehensive synthesis currently maps national governance approaches against established principles or documents implementation realities across diverse contexts.

Objective:

This scoping review aims to: (1) characterize national approaches to AI governance in health; (2) assess alignment with established governance principles; and (3) identify implementation challenges and enabling factors.

Methods:

Following Arksey and O’Malley’s framework and PRISMA-ScR guidelines, we searched six databases and key grey literature repositories for sources published between January 2015 and April 2025. Eligible documents include national-level policies, empirical analyses, and official reports on AI governance in health. Data extraction is guided by a framework integrating World Health Organization AI ethics and governance guidance and the strategic priorities of the Global Initiative on AI for Health across four dimensions—Ethics, Regulation, Implementation, and Operations. Descriptive mapping, governance principal coding, thematic synthesis, and subgroup analyses will be conducted.

Results:

From 17,023 database records and 972 grey literature items, 149 sources met inclusion criteria. Quality assessment will be completed by November 2025, with full data extraction by December 2025. Initial mapping shows highly variable national governance structures and uneven incorporation of ethical, regulatory, and operational principles.

Conclusions:

This review addresses a critical evidence gap by providing a comprehensive mapping of national AI governance policies in health against established governance principles. Findings will inform evidence-based, equitable, and context-specific governance frameworks essential for safe and trustworthy AI integration in health systems.