Artificial Intelligence in Recurrent Pregnancy Loss: From Risk Prediction to ART Translation

Miscarriage is a common adverse reproductive outcome, and recurrent pregnancy loss (RPL) remains a major challenge in reproductive medicine. Despite advances in genetics, immunology, endocrinology, and endometrial biology, many RPL cases remain unexplained. Conventional statistical approaches may be limited in capturing high-order nonlinear interactions among clinical, imaging, immunological, and molecular factors associated with pregnancy loss unless these interactions are explicitly modeled. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), has therefore been investigated as a potential framework for reproductive risk prediction and patient stratification. This narrative review summarizes current evidence on AI-based prediction of miscarriage and RPL, with emphasis on its possible translational relevance to infertility treatment and assisted reproductive technology (ART). Clinical data-driven models have shown potentially useful discriminatory performance, while biomarker-integrated ML approaches suggest that immune-inflammatory signatures may contribute to risk estimation. Imaging-based AI, including radiomics from multimodal ultrasound, may also support noninvasive assessment of endometrial receptivity and inform embryo transfer planning. In parallel, the broader ART literature suggests increasing interest in AI for embryo selection, embryology laboratory workflow, and ovarian stimulation prediction. However, the evidence remains limited by retrospective study designs, small datasets, inconsistent RPL definitions, inadequate external validation, and concerns regarding interpretability, fairness, and regulation. Further progress will require multimodal, explainable, and prospectively validated systems linked to clinically meaningful outcomes. AI may ultimately support more individualized reproductive care, but routine clinical implementation remains premature.