OBSTETRIDE YAPAY ZEKÂ: BIBLIYOMETRIK BIR ANALIZ

Abstract

Objective: Artificial intelligence (AI) technologies have advanced rapidly and are increasingly being used in maternity care. This study aims to map the literature on AI use during pregnancy, childbirth, and the postpartum period through bibliometric analysis and to highlight global research trends and their impact on primary care.Methods: A literature review was conducted using the Web of Science Core Collection, Scopus, PubMed, IEEE Xplore, and Embase databases. The search strategy combined keywords related to childbirth and AI. Eligible studies were analyzed using VOSviewer, R software, and Microsoft Excel to analyze trends in the included articles.Results: A total of 254 publications were included. Publications increased nearly fourfold after 2019, reaching 90 articles in 2024. The United States led in publication productivity and citations, while Italy and Spain showed the highest citation impact per publication, with strong collaboration involving China, the United States, and the United Kingdom. Artificial intelligence, machine learning, and deep learning were the dominant themes, mainly applied to high-risk pregnancy diagnosis and birth management.Conclusion: In order to advance the subject globally, more research and international collaboration are required, as this study identifies important research issues and a rising but still small body of literature in AI for obstetrics.The findings underscore the importance of strengthening primary-care integration, supporting clinician training, and encouraging broader global collaboration to enhance the safe and equitable use of AI in maternal care.

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