Artificial intelligence methods in gestational diabetes mellitus prediction: A systematic literature review

Gestational diabetes mellitus (GDM) is the most common metabolic disorder in pregnancy, posing risks to both maternal and neonatal health. Artificial intelligence (AI) and machine learning (ML)-based solutions hold the promise of improving GDM prediction, thus enabling earlier and more personalized care. The main objective of this systematic review is to provide a comprehensive overview of AI/ML methods used for GDM prediction, leveraging the data from both the preconception and pregnancy periods. We conducted a PRISMA-guided search across databases including PubMed, Scopus, IEEE, and Web of Science from their inception to May 27th 2024. Studies were included if they applied AI/ML methods to predict GDM and were published and peer-reviewed. We extracted data across 30 dimensions. We performed a dual-framework quality assessment of included studies using PROBAST and the IJMEDI checklist. A total of 78 studies met the inclusion criteria. Logistic regression (46 studies), tree-based models (41 studies), and support vector machines (29 studies) were the most frequently used AI methods. Neural networks were most often reported as best-performing (15 studies), followed by boosting (14 studies), and tree-based methods (13 studies). Twelve studies included preconception data. Clinically relevant metrics such as sensitivity, specificity, and calibration were frequently underreported, with decision-curve analysis rarely applied. Thirteen studies performed external validation, and very few employed causal or explainable modeling approaches. Risk of bias was high in most studies. According to the IJMEDI checklist, most studies insufficiently addressed data preparation, validation, and deployment aspects. AI-based GDM prediction is rapidly evolving, with strong potential for earlier and more personalized interventions. Future work should prioritize transparent reporting, external validation, and development of trustworthy, explainable models using diverse, longitudinal data. Closer collaboration among data scientists, clinicians, and healthcare systems is needed to close the loop from AI innovation to clinical practice.