Abstract
Local use of artificial intelligence offers opportunities for improved global health outcomes. Global health ecosystems comprising mobile applications include generative Artificial Intelligence (AI) capabilities that support public health in low and middle-income communities around the world. However, challenges remain in the datasets, training and use of the Large Language Models used to support public health interventions. Following a review of the use of artificial intelligence in global health, this paper addresses some of the most difficult challenges of implementing generative artificial intelligence in global health, that of algorithmic bias, and offers a model of collective governance that involves local participation in the creation of the data sets and training to ensure algorithmic accountability. This conceptualization of global health involves access to resource networks through which health diagnosis, interventions and treatments may be carried out at any place at any time. It offers a policy framework to ensure that AI serves as a catalyst for equitable and sustainable development in public health and healthcare to address the existing disparities which in a principled framework for its design, governance, and implementation is essential for global public health. This framework emphasizes local ownership, human-centricity, and a proactive approach to ethical considerations, moving beyond a model where AI is genuinely designed, governed, and implemented by the low- and middle-income communities globally, with a specific focus on health outcomes. The contribution of this policy paper is in a novel approach to public health that involves the co-creation and collective responsibility for governance in global health systems.