Evidence integration: The transformative role of artificial intelligence in maternal health

Global maternal health outcomes remain inequitable, particularly in low- and middle-income countries, due to persistent gaps in access, care continuity, and postpartum follow-up. Digital health tools, especially mobile health and artificial intelligence, offer promising avenues to enhance education, monitoring, risk stratification, and clinical decision-support.

This umbrella review synthesizes evidence from existing reviews on AI applications in maternal health, focusing on: (1) AI for predicting and stratifying risks of pregnancy-related complications and mortality, (2) AI-driven preventive and supportive interventions from pregnancy through postpartum, and (3) implementation barriers to real-world scale-up. It also proposes a practical roadmap for developing a scalable AI-enabled maternal health solutions.We searched PubMed, Scopus, and Web of Science for English-language reviews (2000-2025). Two reviewers independently screened records, assessed eligibility, and evaluated methodological quality using AMSTAR 2; low-quality reviews were excluded.

Thirty-four reviews were included. Commonly employed AI methods included logistic regression, random forests, gradient boosting, support vector machines, and deep learning. Evidence indicates AI's potential for risk prediction in hypertensive disorders, gestational diabetes, preterm birth, perinatal mental health, and other complications. However, a significant translation gap persists: external validation, model interpretability, clinical workflow integration, fairness auditing, and prospective impact evaluation were inconsistently addressed.

This umbrella review suggests that AI tools can predict pregnancy complications, but a major translation gap remains-lack of external validation, interpretability, workflow integration, fairness audits, and prospective impact assessment. Addressing these gaps is essential for equitable scale-up, especially in low- and middle-income countries. We propose a practical roadmap to guide future development from pregnancy through postpartum.