Authors: Anbazhagan R, Dr. Susitha N
Abstract: Pregnancy-related complications such as gestational diabetes, preeclampsia, preterm birth, and fetal distress remain major causes of maternal and perinatal morbidity, and timely detection depends on regular, accurate monitoring that many women cannot easily access. Advances in wearable sensing, mobile health, and artificial intelligence (AI) have made continuous, data-driven pregnancy monitoring technically feasible. This paper presents a structured narrative review of AI-based pregnancy health monitoring, drawing mainly on recent systematic, scoping, and narrative reviews together with selected primary studies. It examines the data sources and sensing modalities used, the machine learning and deep learning techniques applied, and the main application areas: gestational diabetes and preeclampsia prediction, fetal monitoring through cardiotocography, preterm birth and imaging, and wearable and IoT-based remote monitoring.The review finds that AI methods show consistent promise across these areas, but that the evidence base is limited by small single-centre datasets, geographic concentration of research, class imbalance, scarce external validation, and an unresolved tension between accuracy and interpretability. Privacy, sensor accuracy, and user compliance further constrain real-world deployment. The paper proposes a five-layer reference architecture for an AI-based pregnancy health monitoring system and outlines research directions relevant to resource-constrained settings such as India.
DOI: https://doi.org/10.5281/zenodo.23417250
