ARTIFICIAL INTELLIGENCE IN OBSTETRICS: A BIBLIOMETRIC ANALYSIS

Year 2026, Volume: 20 Issue: 1, 78 - 89, 21.02.2026

Elif Doğan , Betül Uncu , Sema Yılmaz

https://izlik.org/JA28GF42NH

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

https://izlik.org/JA28GF42NH

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

References

• 1. Wael A, Madi A. Accelerating artificial intelligence: the role of GPUs in deep learning and computational advancements. East J Eng. 2025;1(1):31–46. doi:10.63496/eje.Vol1.Iss1.34
• 2. Rani S, Kataria A, Bhambri P, Pareek PK, Puri V. Artificial intelligence in personalized health services for better patient care. In: Revolutionizing Healthcare: AI Integration with IoT for Enhanced Patient Outcomes. Cham, Switzerland: Springer Nature; 2024:89–108.
• 3. Crossnohere NL, Elsaid M, Paskett J, et al. Guidelines for artificial intelligence in medicine: literature review and content analysis of frameworks. J Med Internet Res. 2022;24(8):e36823. doi:10.2196/36823
• 4. Kulkarni S, Seneviratne N, Baig MS, Khan AHA. Artificial intelligence in medicine: where are we now?. Acad Radiol. 2020;27(1):62–70. doi:10.1016/j.acra.2019.10.001
• 5. Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021;139(1):4–15. doi:10.1093/bmb/ldab016
• 6. Iftikhar P, Kuijpers MV, Khayyat A, Iftikhar A, De Sa MD. Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice. Cureus. 2020;12(2):e7124. doi:10.7759/cureus.7124.
• 7. Shobarani R, Dhivya P, Radha D, Kavitha R, Suganthi T. AI at the womb’s edge: transformative technologies in fetal monitoring. In: Modernizing Maternal Care With Digital Technologies. 2024:127–158.
• 8. Bayomi SM, Mohammed SH, Sayed FM, Farahat HEA, Atia NS, Ragab M. Artificial intelligence in primary and community care: nursing interventions to improve maternal health outcomes. Vasc Endovasc Rev. 2025;8(3 Suppl):212–220.
• 9. Ike PC, Atuahene C, Nwachukwu SF, et al. Human–AI collaboration in maternal mental health: a hybrid approach to reducing anxiety in expectant and nursing mothers. Int J Org Med Dev Res. 2025;10(3). doi:10.70382/mejomdr.v10i3.014
• 10. Fischer A, Rietveld A, Teunissen P, Hoogendoorn M, Bakker P. What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals. BMJ Open. 2023;13(10):e076017. doi:10.1136/bmjopen-2023-076017
• 11. Klarin A. How to conduct a bibliometric content analysis: guidelines and contributions of content co-occurrence or co-word literature reviews. Int J Consum Stud. 2024;48(2):e13031. doi:10.1111/ijcs.13031
• 12. Passas I. Bibliometric analysis: the main steps. Encyclopedia. 2024;4(2):1014–25. doi:10.3390/encyclopedia4020065
• 13. Barrington NM, Gupta N, Musmar B, et al. A bibliometric analysis of the rise of ChatGPT in medical research. Med Sci (Basel). 2023;11(3):61. doi:10.3390/medsci11030061
• 14. Olorunsogo TO, Balogun OD, Ayo-Farai O, et al. Reviewing the evolution of US telemedicine post-pandemic by analyzing its growth, acceptability, and challenges in remote healthcare delivery during global health crises. World J Biol Pharm Health Sci. 2024;17(1):75–90. doi:10.30574/wjbphs.2024.17.1.0010
• 15. Correia V, Mascarenhas T, Mascarenhas M. Smart pregnancy: AI-driven approaches to personalised maternal and foetal health—a scoping review. J Clin Med. 2025;14(19):6974. doi:10.3390/jcm14196974
• 16. Khushk A, Yi X, Zhiying L, Seow RYC. How Chinese digital healthcare is different from the United States? A systematic review. Technol Anal Strateg Manag. 2025;37(13):4722–4733. doi:10.1080/09537325.2025.2469671
• 17. da Silva RGL. The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies. Global Health. 2024;20(1):44. doi:10.1186/s12992-024-01049-5
• 18. Tijssen R. Research cooperation between universities and local firms: comparing university ecosystems in China and the USA. J Knowl Econ. 2025. Published online. doi:10.1007/s13132-025-02588-x
• 19. Kijewski S, Ronchi E, Vayena E. International organisations and the global governance of AI in health. In: Solaiman B, Cohen IG, eds. Research Handbook on Health, AI and the Law. Cheltenham, UK: Edward Elgar Publishing; 2024:Chap 15. doi:10.4337/9781802205657.ch15
• 20. De Nicolò V, La Torre D. Global research trends of artificial intelligence and machine learning applied in medicine: a bibliometric analysis (2012–2022). In: Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications. Academic Press; 2024:151–177. doi:10.1016/B978-0-443-13671-9.00004-1
• 21. Yeo L, Romero R. Fetal intelligent navigation echocardiography (FINE): a novel method for rapid, simple, and automatic examination of the fetal heart. Ultrasound Obstet Gynecol. 2013;42(3):268–84. doi:10.1002/uog.12563
• 22. Caballero-Ruiz E, García-Sáez G, Rigla M, et al. A web-based clinical decision support system for gestational diabetes: automatic diet prescription and detection of insulin needs. Int J Med Inform. 2017;102:35–49. doi:10.1016/j.ijmedinf.2017.02.014
• 23. Romeo V, Ricciardi C, Cuocolo R, et al. Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa. Magn Reson Imaging. 2019;64:71–6. doi:10.1016/j.mri.2019.05.017
• 24. Fergus P, Selvaraj M, Chalmers C. Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using cardiotocography traces. Comput Biol Med. 2018;93:7–16. doi:10.1016/j.compbiomed.2017.12.002
• 25. Grünebaum A, Chervenak J, Pollet SL, et al. The exciting potential for ChatGPT in obstetrics and gynecology. Am J Obstet Gynecol. 2023;228(6):696–705. doi:10.1016/j.ajog.2023.03.009
• 26. Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol. 2020;56(4):498–505. doi:10.1002/uog.22122
• 27. Zhao Z, Deng Y, Zhang Y, et al. DeepFHR: intelligent prediction of fetal acidemia using fetal heart rate signals based on convolutional neural network. BMC Med Inform Decis Mak. 2019;19(1):286. doi:10.1186/s12911-019-1007-5
• 28. Varanini M, Tartarisco G, Billeci L, et al. An efficient unsupervised fetal QRS complex detection from abdominal maternal ECG. Physiol Meas. 2014;35(8):1607–19. doi:10.1088/0967-3334/35/8/1607
• 29. Akbulut A, Ertugrul E, Topcu V. Fetal health status prediction based on maternal clinical history using machine learning techniques. Comput Methods Programs Biomed. 2018;163:87–100. doi:10.1016/j.cmpb.2018.06.010
• 30. Namburete AI, Stebbing RV, Kemp B, et al. Learning-based prediction of gestational age from ultrasound images of the fetal brain. Med Image Anal. 2015;21(1):72–86. doi:10.1016/j.media.2014.12.006
• 31. Rani S, Kumar R, Panda BS, et al. Machine learning-powered smart healthcare systems in the era of big data: applications, diagnostic insights, challenges, and ethical implications. Diagnostics (Basel). 2025;15(15):1914. doi:10.3390/diagnostics15151914
• 32. Ngepah N, Saba CS, Mouteyica AEN, Ohonba A. The impact of artificial intelligence on maternal mortality: evidence from global, developed and developing countries. Global Health. 2025;21(1):41. doi:10.1186/s12992-025-01135-2
• 33. El Arab RA, Al Moosa OA, Albahrani Z, et al. Integrating artificial intelligence into perinatal care pathways: a scoping review of reviews of applications, outcomes, and equity. Nurs Rep. 2025;15(8):281. doi:10.3390/nursrep15080281
• 34. Serin O, Yıldırım BF, Duman F, et al. Physician perspectives on artificial intelligence in healthcare: a cross-sectional study of child-focused care in a Turkish tertiary hospital. Int J Med Inform. 2025;203:106003. doi:10.1016/j.ijmedinf.2025.106003