Аннотация
synthesizes current evidence on AI applications for predicting major obstetric complications, including preeclampsia, preterm birth, gestational diabetes, and fetal growth restriction. Reported predictive performance across studies demonstrates considerable heterogeneity, with area under the receiver operating characteristic curve (AUROC) values ranging from approximately 0.73 to 0.97, reflecting differences in datasets, model architectures, and validation strategies. Beyond predictive accuracy, this review critically examines sources of algorithmic bias that may influence model performance and equity in maternal healthcare. Eight key bias mechanisms are identified, including sampling bias, measurement bias, algorithmic bias, temporal bias, selection bias, labelling bias, deployment context bias, and access bias. These biases may limit model generalizability and risk amplifying existing maternal health disparities, particularly in low- and middle-income countries. Current evidence is further constrained by limited external validation across diverse populations, the absence of prospective clinical impact trials, insufficient cost-effectiveness analyses, and evolving regulatory frameworks governing AI accountability. The review discusses potential pathways for responsible clinical translation, emphasizing inclusive dataset development, rigorous multisite validation, careful integration into clinical workflows with human oversight, and strengthening regulatory and workforce capacity. Achieving equitable implementation of AI in maternal health will require deliberate efforts to embed transparency, accountability, and health equity throughout the AI development and deployment lifecycle.