Artificial Intelligence in the Early Diagnosis of Obstetric Emergencies

ABSTRACT: This research addresses the application of artificial intelligence in obstetric emergencies, focusing on technological advances and operational challenges to improve maternal outcomes. It is contextualized in the growing use of predictive models and intelligent systems in maternal health, amid clinical and organizational complexities. The problem focuses on the effectiveness, integration and ethics of adopting these systems in emergency settings, where fast and accurate decisions are crucial. The general objective was to analyze, under the Giftedean neoperspectivist paradigm, the impact of artificial intelligence in obstetric emergencies, integrating theories of complexity, systems and decision. The hypothetical-deductive method was used to structure the research, with a rigorous bibliographic and documentary narrative review, consulting databases such as PubMed, Scopus and Web of Science, using descriptors related to artificial intelligence, obstetric emergency and clinical prediction. Initially, approximately 1,500 studies were identified, of which 85 were selected for in-depth analysis. The main findings highlight the effectiveness of machine learning models in early diagnosis, the importance of technological integration, and the institutional and ethical barriers present. It is concluded that artificial intelligence represents a significant advance for emergency obstetric care, although gaps related to data quality, cultural adaptation, and technological governance persist. Methodological limitations include the focus on secondary studies and the absence of controlled empirical tests. The contributions include the formulation of an innovative theoretical-methodological framework and practical evidence for health policies. The added value lies in the provision of subsidies to improve maternal safety, promote inclusion, and foster future research in digital health.