Ethics and Governance of Artificial Intelligence in Healthcare: A Global Scoping Review

This scoping review aims to comprehensively map and synthesise global literature on the ethics and governance of artificial intelligence (AI) in healthcare. AI technologies are increasingly integrated into clinical decision support, diagnostics, predictive analytics, and health system management, offering substantial potential benefits while simultaneously raising complex ethical, legal, and governance challenges. Key concerns include transparency, accountability, bias, data privacy, patient autonomy, and public trust in AI-enabled healthcare. International organisations such as WHO, UNESCO, and OECD have developed guidance promoting human-centred and rights-based approaches to AI governance, yet significant variability exists in how these principles are interpreted and implemented across countries, particularly in low- and middle-income settings. This review will identify, describe, and synthesise literature addressing ethical issues, governance mechanisms, and regulatory frameworks for AI in healthcare across global contexts. Eligible sources will include empirical studies, conceptual and theoretical papers, policy documents, and grey literature relevant to healthcare, public health, and digital health. The methodology will follow Arksey and O’Malley’s scoping review framework, with refinements by Levac et al. and the Joanna Briggs Institute, and will adhere to PRISMA-ScR guidance. Searches will be conducted in major bibliographic databases and supplemented with policy and regulatory sources from international and national institutions. Data will be charted using a standardised form and analysed using descriptive numerical summaries, thematic analysis, and evidence mapping. The expected outcome of this review is a comprehensive evidence map of how ethics and governance of AI in healthcare are conceptualised, debated, and operationalised worldwide. The review will identify dominant ethical themes, governance models, and regulatory gaps, and will compare approaches between high-income countries and low- and middle-income countries. By consolidating current knowledge and highlighting areas of unmet need, this work is expected to inform policymakers, healthcare leaders, regulators, and researchers seeking to develop contextually appropriate, ethically robust, and practically implementable governance frameworks for AI in healthcare, ultimately supporting responsible and equitable AI adoption.