The health needs of those living in resource-limited settings are a vastly
overlooked and understudied area in the intersection of machine learning (ML)
and health care. While the use of ML in health care is more recently
popularized over the last few years from the advancement of deep learning,
low-and-middle income countries (LMICs) have already been undergoing a digital
transformation of their own in health care over the last decade, leapfrogging
milestones due to the adoption of mobile health (mHealth). With the
introduction of new technologies, it is common to start afresh with a top-down
approach, and implement these technologies in isolation, leading to lack of use
and a waste of resources. In this paper, we outline the necessary
considerations both from the perspective of current gaps in research, as well
as from the lived experiences of health care professionals in resource-limited
settings. We also outline briefly several key components of successful
implementation and deployment of technologies within health systems in LMICs,
including technical and cultural considerations in the development process
relevant to the building of machine learning solutions. We then draw on these
experiences to address where key opportunities for impact exist in
resource-limited settings, and where AI/ML can provide the most benefit.Comment: Accepted Paper at ICLR 2020 Workshop on Practical ML for Developing
Countrie
