Background Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication that can pose numerous adverse health effects on both mothers and newborns. Accurate prediction of the risk of GDM serves as a valuable supplement to prenatal education and clinical decision-making. Compared with traditional prediction models, artificial intelligence (AI) algorithms have demonstrated higher predictive accuracy and stronger individualization capabilities. However, the application of AI models in GDM prediction is still in a developmental stage, and their performance and clinical utility have not been thoroughly evaluated. Therefore, this study aims to systematically review and critically appraise the published predictive performance of AI models for GDM prediction and to offer insights for future research and practical application. Methods A systematic literature search will be performed across six databases (PubMed, Web of Science, Cochrane Library, Scopus, EMBASE, and OVID). Screening of titles and abstracts, full-text review, and data extraction will be independently completed by two authors. Qualitative data on the characteristics of the included studies, methodological quality, and the applicability of models will be summarized through narrative descriptions and tabulated formats. For models with predictive performance data from multiple studies, a random-effects meta-analysis or meta-regression will be employed to synthesize the findings, considering potential heterogeneity. Ethics and dissemination Ethical approval is deemed not applicable for this systematic review and meta-analysis. The findings will be based on published literature, disseminated through publication in a peer-reviewed journal, and presented at major conferences focused on clinical healthcare. Systematic review registration PROSPERO registration number CRD42025645913
Liang et al. (2026) studied this question.