Artificial Intelligence for Early Detection of Preeclampsia and Gestational Diabetes Mellitus: A Systematic Review of...

Preeclampsia (PE) and gestational diabetes mellitus (GDM) are major contributors to maternal and neonatal morbidity and mortality. Early detection is critical, yet current approaches, such as clinical risk scores for PE and glucose challenge/oral glucose tolerance test (OGTT) screening for GDM, often show limited sensitivity and variable predictive accuracy. Artificial intelligence (AI) and machine learning (ML) offer promising avenues for enhancing early prediction and diagnosis. This systematic review, conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, synthesized evidence from five databases (PubMed, Scopus, Embase, IEEE Xplore, ACM Digital Library) covering January 2020-July 2025. Eligible studies included both model development and validation efforts in pregnant populations. Data were extracted on study characteristics, AI model types, and diagnostic performance metrics. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Nine studies met the inclusion criteria, reflecting strict eligibility requirements and limited high-quality research in this area. AI models frequently achieved strong performance, with area under the curve (AUC) values often >0.85. For PE, a neural network model externally validated in Spain achieved AUCs of 0.920 and 0.913 for early and preterm PE, with sensitivity up to 84%. For GDM, an XGBoost model achieved an AUC of 0.946 with an accuracy of 87.5%, while a Random Forest model reached a sensitivity of 75-85% and a specificity of 88-91%. Ensemble methods generally outperformed logistic regression. Seven studies were judged low risk of bias, while two were high risk, particularly in participant selection and analysis domains. Several models also demonstrated good calibration and positive net benefit on decision curve analysis, comparable to established clinical tools. AI models show substantial potential for early detection of PE and GDM, though heterogeneity and limited external validation remain barriers. Future research should prioritize multicenter, prospective validation, standardized reporting, and attention to equity and generalizability to ensure safe and effective translation into clinical practice.