Artificial Intelligence for Global Health: Learning From a Decade of\n Digital Transformation in Health Care

Abstract

The health needs of those living in resource-limited settings are a vastly\noverlooked and understudied area in the intersection of machine learning (ML)\nand health care. While the use of ML in health care is more recently\npopularized over the last few years from the advancement of deep learning,\nlow-and-middle income countries (LMICs) have already been undergoing a digital\ntransformation of their own in health care over the last decade, leapfrogging\nmilestones due to the adoption of mobile health (mHealth). With the\nintroduction of new technologies, it is common to start afresh with a top-down\napproach, and implement these technologies in isolation, leading to lack of use\nand a waste of resources. In this paper, we outline the necessary\nconsiderations both from the perspective of current gaps in research, as well\nas from the lived experiences of health care professionals in resource-limited\nsettings. We also outline briefly several key components of successful\nimplementation and deployment of technologies within health systems in LMICs,\nincluding technical and cultural considerations in the development process\nrelevant to the building of machine learning solutions. We then draw on these\nexperiences to address where key opportunities for impact exist in\nresource-limited settings, and where AI/ML can provide the most benefit.\n

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