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

• Type of Material: Article
• Authors: Varoon Mathur, New York University; Saptarshi Purkayastha, Indiana University-Purdue University Indianapolis; Judy Gichoya, Emory University
• Language: English
• Date: 2020
• Publisher: arXiv
• Publication Version: Final Published Version
• Copyright Statement: authors
• Title of Journal or Parent Work: ICLR 2020
• Keywords: middle income; low income; health needs; health care; machine learning; limited resources; mobile health
• Research Categories: Sociology, Public and Social Welfare; Economics, Finance; Health Sciences, Health Care Management

Abstract

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.