Artificial intelligence for early prediction of gestational diabetes mellitus and preeclampsia: a systematic review of machine learning models and clinical decision support systems.

Gestational diabetes mellitus (GDM) and preeclampsia are among the most significant pregnancy complications, affecting approximately 5-15% and 2-8% of pregnancies worldwide, respectively. These disorders share overlapping metabolic, vascular, inflammatory, and placental mechanisms, highlighting the need for integrated approaches to early prediction and risk assessment. However, existing artificial intelligence (AI)-based prediction models generally address GDM and preeclampsia independently and are often limited by inadequate multimodal data integration, insufficient external validation, and limited model interpretability. This systematic review synthesizes recent advances (2020-2026) in AI-based prediction of GDM and preeclampsia, with emphasis on predictive methodologies, data modalities, validation strategies, and potential clinical applications. The review was conducted in accordance with the PRISMA 2020 guidelines, and 120 studies employing machine learning (ML), deep learning (DL), and hybrid AI approaches using clinical, biochemical, electronic health record (EHR), and multimodal data were included. Across the reviewed studies, AI-based models demonstrated promising predictive performance, with reported area under the receiver operating characteristic curve (AUC) values ranging from 0.70 to 0.95. Ensemble and deep learning approaches generally outperformed conventional statistical methods, particularly when multimodal data were integrated. Frequently identified predictive variables included maternal clinical characteristics, metabolic biomarkers, inflammatory biomarkers, and angiogenic markers such as soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF). Nevertheless, important methodological challenges remain, including limited external validation, substantial data heterogeneity, insufficient model interpretability, inconsistent reporting practices, and limited integration into routine clinical workflows. Furthermore, most existing AI models predict GDM or preeclampsia independently despite their shared pathophysiological mechanisms, highlighting an important gap in current prediction research. This review provides a comprehensive synthesis of epidemiological, clinical, mechanistic, and AI-based evidence and proposes an evidence-informed conceptual framework that integrates multimodal data, mechanism-aware modeling, explainable AI, standardized validation, and clinical decision-support considerations. Rather than representing a validated predictive system, the proposed framework provides a conceptual foundation to guide future AI model development, prospective validation, and clinical evaluation. Overall, the findings highlight key opportunities and remaining challenges for developing robust, interpretable, and generalizable AI-based prediction models to support future precision maternal healthcare and improve maternal and neonatal outcomes.