Artificial Intelligence in Feto-maternal Health: A Systematic Review of Predictive Models, Validation, and Clinical Translation

Maternal mortality remains a major global challenge, especially in developing countries. This review assessed Artificial Intelligence applications in Feto-Maternal health, focusing on validation, performance, and implementation. Following PRISMA guidelines and PROSPERO registration (CRD42023347209), we searched PubMed, EMBASE, Cochrane Library, and Web of Science for studies (2000–2023) applying AI to maternal or neonatal outcomes with defined methodology and validation. Of 14,049 studies, 72 met inclusion criteria; 38 (52.8%) reported external validation, and 11 (15.3%) involved experimental or interventional use. Most datasets (81.3%) were from high-income countries, mainly Asia and North America. Frequently assessed outcomes included preterm birth, low birth weight, and neonatal mortality. Random Forest and XGBoost were most used. Internal performance was strong (AUC 0.82; accuracy 89%), with modest declines in external validation (AUC 0.80; accuracy 87%) and reduced sensitivity in real-world settings. AI shows promise but requires rigorously validated, context-specific models, ethical oversight, and readiness for LMIC integration.