DefinePK

Abstract

This systematic review and meta-analysis evaluates the performance of artificial intelligence (AI) models in predicting two major pregnancy complications: preeclampsia and gestational diabetes mellitus (GDM). Adhering to PRISMA guidelines, we analyzed 13 studies from PubMed, Scopus, Web of Science, and IEEE Xplore, selected from an initial pool of 2,163 articles. Using R software (version 4.3.1), we conducted a random-effects meta-analysis, assessing metrics such as the area under the curve (AUC), sensitivity, specificity, and accuracy. The study demonstrated strong predictive performance for preeclampsia and gestational diabetes mellitus (GDM) using artificial intelligence (AI) models. For preeclampsia prediction, the training area under the curve (AUC) was 0.878, while the test AUC was 0.861. Similarly, for GDM, the training AUC was 0.779, and the test AUC was 0.800, indicating high discriminative ability. Tree-based and neural network models outperformed other approaches, particularly when incorporating multimodal data—such as clinical and biochemical data or electronic health records (EHR). Sensitivity analysis further supported these findings, even after excluding high-risk studies identified by the PROBAST tool. While AI models show promise for antenatal risk screening, challenges remain, including limited external validation and interpretability. Future research should focus on improving model transparency, ensuring diverse ethnic representation, and facilitating seamless integration into clinical practice. These steps are critical to harnessing AI's potential for enhancing maternal and fetal health outcomes.