Artificial Intelligence in Obstetric Anesthesia for Hypertensive Disorders of Pregnancy: A Narrative Review

Abstract

Background: Hypertensive disorders of pregnancy, including preeclampsia and eclampsia, represent a significant global maternal health challenge, affecting 2-8% of pregnancies worldwide and contributing to 11-14% of maternal deaths. The complex, multisystemic nature of these conditions, coupled with their unpredictable clinical trajectories, presents substantial challenges for anesthetic management. This narrative review examines the emerging integration of artificial intelligence (AI) technologies in obstetric anesthesia care for hypertensive disorders, exploring current applications, technological foundations, implementation challenges, and future directions. We synthesize evidence demonstrating AI's potential in continuous monitoring, predictive analytics, personalized care delivery, and clinical decision support. While significant barriers to implementation exist-including technological, regulatory, and ethical considerations-the integration of AI into obstetric anesthesia represents a paradigm shift toward precision, predictive, and personalized maternal care. This narrative review synthesizes current evidence to argue that the primary value of AI in this context is not the replacement of clinical judgment, but its augmentation through the synthesis of high-dimensional, time-series data, thereby enhancing anesthesiologist situation awareness and enabling proactive, rather than reactive, management.

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