Maternal Risk Prediction Models in Africa: A Scoping Review of Approaches, Variables, and Contextual Gaps

Background Maternal mortality remains unacceptably high in many low- and middle-income countries, particularly in sub-Saharan Africa, despite global commitments to its reduction. In recent years, artificial intelligence and machine learning–based predictive models have been increasingly applied to maternal health, with the aim of identifying women at high risk of adverse outcomes. However, the scope, design, and contextual relevance of these models remain unclear. Objective This scoping review aimed to map existing evidence on maternal risk prediction models, examine the types of outcomes and predictors used, identify methodological trends and gaps, and propose a conceptual framework to guide the development of more context-sensitive predictive approaches. Methods A scoping review was conducted following established methodological guidance. Peer-reviewed studies and relevant reports focusing on maternal risk prediction, maternal mortality, severe maternal morbidity, and the application of artificial intelligence or machine learning in maternal health were included. Data were charted on study characteristics, data sources, predictors, modeling approaches, predicted outcomes, and performance measures. Findings were synthesized narratively. Results The review identified a growing body of literature applying machine learning techniques to predict maternal mortality, near-miss events, and pregnancy-related complications such as postpartum hemorrhage, preeclampsia, and sepsis. Most models relied predominantly on clinical and obstetric variables and were developed using retrospective, facility-based datasets. Social determinants of health and health system factors were inconsistently incorporated, and external validation across diverse contexts was limited. High-performing models often lacked interpretability, and evidence on real-world implementation was scarce. These gaps highlighted a disconnect between predictive accuracy and practical applicability in maternal health settings. Conclusion Existing maternal risk prediction models demonstrated technical promise but remained limited in scope, contextual sensitivity, and equity orientation. This review highlighted the need for integrated predictive frameworks that incorporate clinical, social, and health system determinants of maternal risk. The proposed conceptual framework provides a foundation for developing more context-aware and actionable predictive models to support timely intervention and reduce preventable maternal deaths.