The Role of Artificial Intelligence in Obstetrics and Gynecology: Current Applications and Future Potential

ABSTRACT: Objective: To evaluate current applications of artificial intelligence (AI) in obstetrics and gynecology (OB/GYN) and project future advancements for enhancing diagnostic accuracy, clinical efficiency, and maternal-fetal outcomes. Methods: A systematic review of peer-reviewed studies (2015–2025) from PubMed, Scopus, and IEEE Xplore was conducted, focusing on AI techniques like convolutional neural networks (CNNs), machine learning (ML), deep learning (DL), and natural language processing (NLP) in prenatal diagnostics, gynecologic oncology, reproductive medicine, and workflow optimization. Performance metrics (sensitivity, specificity, AUC) and limitations were analyzed. Results: AI achieves 94% sensitivity in detecting fetal heart defects via ultrasound, 96% accuracy in cervical cancer screening with Pap smears, and an AUC of 0.89 for preeclampsia prediction using electronic health records. NLP reduces documentation time by 30%, while DL improves IVF success rates by 15%. Challenges include dataset bias (10% lower accuracy for minorities) and clinician distrust (40% skepticism rate). Future potential includes AI-driven telemedicine and personalized IVF protocols. Conclusion: AI significantly enhances OB/GYN care but requires inclusive datasets, regulatory streamlining, and clinician training to ensure equitable, scalable adoption and reduce global maternal mortality by 15–20%. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Obstetrics, Gynecology, Prenatal Diagnostics, Cervical Cancer, IVF, Telemedicine