The Role of Artificial Intelligence in the Radiological Diagnosis of Urogynecological and Obstetric Disorders: A Narrative Review

Artificial intelligence (AI) has emerged as a transformative tool in radiological diagnosis, particularly in urogynaecology and obstetric disorders where accurate and timely imaging is essential. This narrative review evaluates current AI applications across key imaging modalities, including ultrasound and magnetic resonance imaging, emphasizing improvements in diagnostic precision, automation, and workflow efficiency. AI-driven approaches such as machine learning, deep learning, and radiomics enable automated segmentation, anomaly detection, and quantitative assessment of conditions, including pelvic floor dysfunction, urinary incontinence, endometriosis, and fetal abnormalities. In obstetric imaging, AI supports automated fetal biometry, anomaly detection, and risk prediction for complications such as preeclampsia and preterm birth. Integration of AI into radiological workflows enhances reporting efficiency and facilitates clinical decision-making through predictive analytics and standardized outputs. Challenges related to data quality, model generalizability, interpretability, and regulatory frameworks continue to limit widespread clinical adoption. Future directions should focus on multicenter datasets, explainable AI, and multimodal integration to improve reliability and clinical translation. Furthermore, strengthening clinician-AI collaboration and incorporating real-world validation studies will be critical for safe implementation. Overall, AI has significant potential to enhance diagnostic accuracy, reduce variability, and support personalized care in women’s imaging.