Predictive AI Systems for Maternal and Infant Health

Artificial Intelligence (AI)

Maternal and infant health remains a cornerstone of public health efforts, particularly in low- and middle-income countries where access to timely and high-quality care is often limited. Despite significant advancements in healthcare systems, preventable complications during pregnancy and infancy continue to result in high morbidity and mortality rates. Predictive Artificial Intelligence (AI) systems are rapidly emerging as transformative tools in this domain, enabling early identification of at-risk pregnancies, preterm births, neonatal conditions, and postpartum complications. By harnessing large datasets from electronic health records (EHRs), wearable devices, imaging systems, and genomic information, AI can uncover subtle patterns that precede health deterioration. This paper explores how predictive AI models enhance maternal and infant health outcomes through early detection, remote monitoring, risk stratification, and personalized interventions. It also discusses the ethical, technological, and infrastructural considerations involved in deploying AI in maternal-child healthcare and outlines the potential for equitable, scalable, and life-saving applications of predictive technologies across diverse populations.

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