On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review - Khan, Misaal; Khurshid, Mahapara; Vatsa, Mayank; Singh, Richa; Duggal, Mona; Singh, Kuldeep (Frontiers in Public Health, 2022)
A key challenge for hospitals and medical practitioners in public health settings of low- and-middle-income countries is the availability of appropriate health care facilities for rapid medical diagnosis of chronic and fatal diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutritional diseases is a serious public health issue that leads to many deaths each year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent in Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This paper provides a comprehensive review of the digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.