Publications

Abstract

AI solutions are frequently presented as promising solutions to a wide range of global challenges, including public health. However, the potential they hold for advancing public health and the challenges to their deployment require examination particularly in the global south. Using a narrative synthesis on several recent reports of AI solutions in public health and their challenges, we explain the opportunities and challenges of using AI to address major public health problems, particularly in the global south, and describe the methods and models employed by AI systems. In addition to surveillance and monitoring, AI systems have been deployed for disease screening, early diagnosis and management, health education, epidemic forecasting, and for health program planning and management for a range of public health issues. Challenges persist in the interpretability, explainability, generalizability and feasibility of these systems. Also, regulatory, legal, and ethical oversight have not kept pace with AI development. More significantly, existing gaps in health system infrastructure, information system quality, and workforce capacity impede real world implementation of AI for public health. We explain each of these issues and the challenges they pose to using AI in public health. We recommend more research into real-world implementations, and the addressal of persistent health system constraints, to realise the transformative potential AI holds for public health.