A scoping review of machine learning models for predicting preterm birth and miscarriage: Mapping the landscape and the performance-validation paradox

This scoping review aims to summarize how machine learning (ML) has been applied to predict adverse pregnancy outcomes—particularly preterm birth and miscarriage—by comparing data sources, model designs, and reported performance. Evidence from 40 eligible studies was systematically reviewed. For each, we extracted details on the type of data used (e.g., clinical records, imaging, biomarkers), sample size, ML algorithms, and major performance metrics such as AUC and accuracy. Recurring strengths and weaknesses were identified through thematic analysis. The reviewed studies drew on a wide range of data sources—from large electronic health records (EHRs) to imaging and time-series physiological signals. Tree-based algorithms and support vector machines generally showed strong predictive performance, with several studies reporting AUC values above 0.90. However, most investigations were limited by small, single-center datasets and lacked external validation, raising concerns about generalizability and clinical interpretability. Machine learning approaches could meaningfully improve how clinicians anticipate adverse pregnancy outcomes, but their clinical use remains premature. Progress will depend on studies that include larger and more diverse populations, apply rigorous external validation, and focus on developing models that clinicians can interpret and act upon. • Foster Data Collaboration and Resource Sharing by establishing large, international research networks and shared, benchmark datasets that represent diverse global populations. • Champion Standardization through the adoption of common reporting frameworks (e.g., TRIPOD-ML) and evaluation metrics to enable meaningful cross-study comparisons. • Prioritize Clinical Integration from the Outset by coupling model development with health-economic and workflow analyses to ensure predictions are not only accurate but also cost-effective and actionable for clinicians and patients.