Artificial Intelligence in Women’s Reproductive and Maternal Health: Advancing Prediction, Prevention, and Personalized Care

Women’s reproductive and maternal health spans a wide range of clinical conditions and transitions, including gynecologic disorders, pregnancy, childbirth, and postpartum recovery. Adverse outcomes across these areas often remain difficult to detect, predict, or prevent, owing to heterogeneous clinical presentations, fragmented and multimodal data sources, and considerable variability across individuals, populations, and healthcare settings worldwide. Artificial intelligence (AI) offers a powerful means of integrating complex, high-dimensional data drawn from electronic health records, medical imaging, laboratory and omics investigations, wearable devices, and patient-reported outcomes. When rigorously validated, these approaches can support earlier identification of disease, more accurate risk stratification, targeted preventive interventions, and truly personalized clinical decisions, moving the field beyond fragmented, single-source assessment toward a more integrated and holistic understanding of women’s reproductive and maternal health across the life course, from adolescence through the postpartum period and beyond.In this Research Topic our goal is to bring together multidisciplinary research demonstrating how such AI systems can be rigorously validated, benchmarked against existing clinical practice, and meaningfully translated into everyday care across diverse healthcare settings and populations. The collection welcomes AI applications across three interconnected pillars:• Gynecologic and reproductive disorders - including PMOS (polyendocrine metabolic ovarian syndrome), endometriosis, adenomyosis, uterine fibroids, menstrual disorders, chronic pelvic pain, gynecologic imaging, and surgical risk assessment and outcomes.• Pregnancy, childbirth, and maternal complications - including gestational diabetes, hypertensive disorders and preeclampsia, placental abnormalities, preterm birth, maternal sepsis and deterioration, operative delivery, and early-warning systems for high-risk pregnancies.• Postpartum recovery and long-term maternal health - including postpartum hemorrhage and infection, pelvic floor dysfunction, pain and functional recovery, breastfeeding challenges, sleep and fatigue, postpartum depression and other mental health risks, persistent hypertension and cardiometabolic risk, and hospital readmission.Within these pillars, we invite any clinically relevant, validated AI or machine learning approach, including generative AI, computer vision, natural language processing, and multimodal analysis of imaging, electronic health record, laboratory, genomic, or wearable data, provided it demonstrates clear clinical utility and appropriate validation, such as external testing, calibration, or comparison with current clinical practice and established prediction tools. Studies focused primarily on assisted reproduction or gamete and embryo assessment, fall outside this Topic’s scope. Topic editors: Rawan AlSaad, Zahra Sharifiheris Source: https://www.frontiersin.org/research-topics/85162/artificial-intelligence-in-womens-reproductive-and-maternal-health-advancing-prediction-prevention-and-personalized-care