Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.

Background: Obstetric emergencies, including postpartum hemorrhage, preeclampsia, and preterm birth, remain major contributors to preventable maternal and neonatal morbidity and mortality worldwide. Conventional risk assessment approaches often rely on predefined clinical variables and may insufficiently capture complex nonlinear relationships among maternal characteristics, biomarkers, imaging findings, and electronic health record data. Purpose: This narrative review aimed to synthesize current evidence on the application of artificial intelligence and machine learning for prediction and risk stratification in major obstetric emergencies. Methods: A structured narrative synthesis was conducted to summarize original studies evaluating AI- and ML-based models for postpartum hemorrhage, preeclampsia, and preterm birth. Evidence was reviewed according to target condition, algorithm type, predictor variables, model performance, validation strategy, interpretability, and clinical implementation relevance. Results: Machine-learning models, including random forest, support vector machine, neural networks, gradient boosting, LightGBM, and XGBoost, demonstrated promising discriminatory performance across selected cohorts. These models commonly incorporated maternal demographics, obstetric history, biomarkers, ultrasound parameters, and electronic health record variables. However, substantial heterogeneity in study design, predictors, outcome definitions, and performance reporting limited direct comparison. External validation, calibration assessment, explainability, and prospective evaluation were frequently insufficient. Conclusion: AI and ML may enhance early risk identification in obstetric emergencies, but current evidence remains insufficient for routine clinical implementation. Future studies should prioritize transparent reporting, multicenter validation, calibration, explainable modeling, and prospective assessment of clinical utility.