AI-Enabled Smart Maternal Health Monitoring and Educational Support System for Early Detection and Prevention of High-Risk Pregnancy Complications

Abstract

Pregnancy is a complex physiological process that requires continuous monitoring to ensure the health and safety of both the mother and fetus. Despite significant improvements in maternal healthcare, pregnancy-related complications remain one of the leading causes of maternal and neonatal morbidity and mortality, particularly in low- and middle-income countries. Conditions such as gestational hypertension, preeclampsia, gestational diabetes mellitus, anaemia, preterm labour, fetal growth restriction, and maternal infections often develop gradually and remain undetected until severe complications occur. Early identification and timely intervention are therefore essential to improve maternal and neonatal outcomes. Conventional antenatal care relies heavily on periodic clinical visits and manual assessment, which may fail to detect rapidly developing complications in high-risk pregnancies.

Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), wearable biosensors, cloud computing, and mobile health (mHealth) technologies have transformed maternal healthcare by enabling continuous physiological monitoring, intelligent risk prediction, personalized health education, and remote clinical decision support. AI-enabled smart maternal health monitoring systems integrate real-time physiological data with predictive analytics to identify early warning signs of pregnancy complications before clinical symptoms become severe. Simultaneously, AI-powered educational support platforms provide personalized guidance regarding nutrition, medication adherence, fetal development, lifestyle modifications, danger signs, and appointment reminders, thereby improving maternal awareness and healthcare compliance.

This review comprehensively discusses the integration of AI technologies into maternal healthcare for early detection and prevention of high-risk pregnancy complications. It explores AI algorithms, wearable monitoring devices, predictive modeling, remote patient monitoring, educational interventions, clinical decision support systems, implementation challenges, ethical concerns, and future research directions. The article also highlights the potential of intelligent maternal healthcare systems to reduce maternal mortality, improve pregnancy outcomes, and support sustainable healthcare delivery worldwide.