ARTIFICIAL INTELLIGENCE IN OBSTETRICS: A BIBLIOMETRIC ANALYSIS

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.

Keywords

• Artificial intelligence
• pregnancy
• childbirth
• postpartum
• bibliometric analysis
• obstetrics

OBSTETRİDE YAPAY ZEKÂ: BİBLİYOMETRİK BİR ANALİZ

Abstract

Amaç: Yapay zekâ (YZ) teknolojileri hızla gelişmiş ve anne sağlığı bakımında giderek daha yaygın biçimde kullanılmaya başlanmıştır. Bu çalışmanın amacı, gebelik, doğum ve postpartum dönemde yapay zekâ kullanımına ilişkin literatürü bibliyometrik analiz yoluyla haritalamak ve küresel araştırma eğilimleri ile bunların birinci basamak sağlık hizmetlerine etkisini ortaya koymaktır. Yöntem: Literatür taraması Web of Science Core Collection, Scopus, PubMed, IEEE Xplore ve Embase veri tabanları kullanılarak gerçekleştirildi. Arama stratejisinde doğum ve yapay zekâ ile ilişkili anahtar kelimeler birleştirildi. Uygun bulunan çalışmalar, dâhil edilen makalelerdeki eğilimleri analiz etmek amacıyla VOSviewer, R yazılımı ve Microsoft Excel kullanılarak incelendi. Bulgular: Toplam 254 yayın çalışmaya dâhil edildi. Yayın sayısı 2019 yılından sonra yaklaşık dört kat artarak 2024 yılında 90 makaleye ulaşmıştır. Amerika Birleşik Devletleri yayın üretkenliği ve atıf sayısında ilk sırada yer alırken, İtalya ve İspanya yayın başına en yüksek atıf etkisini göstermiştir; Çin, Amerika Birleşik Devletleri ve Birleşik Krallık arasında güçlü iş birlikleri olduğu belirlenmiştir. Yapay zekâ, makine öğrenmesi ve derin öğrenme baskın temalar olup, bu yaklaşımlar ağırlıklı olarak yüksek riskli gebeliklerin tanılanması ve doğum yönetiminde uygulanmıştır. Sonuç: Bu çalışma, obstetrikte yapay zekâ alanında temel araştırma temalarını ve giderek artmakla birlikte hâlen sınırlı olan literatürü ortaya koymakta; alanın küresel ölçekte ilerletilebilmesi için daha fazla araştırmaya ve uluslararası iş birliğine ihtiyaç olduğunu göstermektedir. Bulgular, anne sağlığı bakımında yapay zekânın güvenli ve adil kullanımını desteklemek amacıyla birinci basamak sağlık hizmetleriyle entegrasyonun güçlendirilmesi, klinisyen eğitimlerinin desteklenmesi ve daha geniş kapsamlı küresel iş birliklerinin teşvik edilmesinin önemini vurgulamaktadır.

Keywords

• Yapay zekâ
• gebelik
• doğum
• postpartum dönem
• bibliyometrik analiz
• obstetrik

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