WAYS TO IMPROVE THE EFFECTIVENESS OF GLOBAL HEALTHCARE GOVERNANCE BASED ON ARTIFICIAL INTELLIGENCE: INTERNATIONAL REGULATORY AND INSTITUTIONAL PERSPECTIVES

Abstract

This article examines ways to improve the effectiveness of global healthcare governance through the application of artificial intelligence (AI), drawing on international regulatory and institutional practices. Effectiveness is understood as the capacity of governance systems to ensure safety and clinical effectiveness, equitable access to healthcare, accountability, and timely cross-border coordination. While AI offers significant opportunities for enhancing clinical decision support, disease surveillance, resource allocation, and biomedical research, it also introduces substantial risks, including algorithmic bias, weak data governance, cybersecurity vulnerabilities, limited transparency, and performance drift of deployed models.

The article situates these challenges within the emerging global governance framework shaped by the World Health Organization, which has issued guidance on the ethics and governance of AI for health as well as regulatory considerations for its deployment. Building on this baseline, the analysis connects key foreign regulatory practices—such as the European Union’s risk-based AI Act, the U.S. Food and Drug Administration’s lifecycle approach through Predetermined Change Control Plans and Good Machine Learning Practice, and harmonization efforts led by the International Medical Device Regulators Forum. The article argues that integrating these approaches can strengthen trust, interoperability, and real-world performance oversight, thereby increasing the overall effectiveness of global healthcare governance in the AI era.