Artificial Intelligence, Machine Learning, and Multi-Omics Biomarker Discovery in Ectopic Pregnancy: A Comprehensive Review.

BACKGROUND: Ectopic pregnancy is a major cause of first-trimester maternal morbidity and mortality, with diagnosis and management posing persistent clinical challenges. This review evaluates the emerging role of artificial intelligence, machine learning, and multi-omics technologies in enhancing diagnosis, treatment prediction, biomarker discovery, and surgical care. MATERIALS AND METHODS: A synthesis of 52 studies (1988-2026) was conducted, assessing machine learning algorithms, proteomic and metabolomic panels, multi-omics integrations, and robotic-assisted surgical outcomes using performance metrics including AUC, sensitivity, and specificity. RESULTS: Machine learning models achieved AUC values up to 0.929 for methotrexate failure prediction and surgical accuracies exceeding 98%. Biomarker panels combining sphingolipids and carnitines demonstrated 100% sensitivity and 95.9% specificity. Multi-omics uncovered GATA4-mediated ITGB3 dysregulation in cesarean scar pregnancy. Robotic-assisted techniques yielded cumulative pregnancy rates over 60% post-reanastomosis. CONCLUSION(S): AI and multi-omics integration offer significant promise for personalized ectopic pregnancy management. However, retrospective designs and small sample sizes limit generalizability, underscoring the need for prospective multicenter validation before routine clinical adoption.