A two-decade bibliometric exploration of AI applications in obstetrics and gynecology (2005-2024)

Aims: Artificial Intelligence (AI) technologies have significantly impacted obstetrics and gynecology (Obs&Gyn), particularly in diagnostics, treatment, and patient care. This study aims to conduct a bibliometric analysis of AI-related research in Obs&Gyn published between January 1, 2005, and December 31, 2024. The main objectives are to explore publication trends, leading contributors, research themes, collaboration patterns, and emerging technologies. Methods: A total of 959 publications were retrieved from the Web of Science Core Collection using the keyword “artificial intelligence” and filtered by the Obs&Gyn category. VOSviewer software was used to map co-authorship, keyword co-occurrence, bibliographic coupling, and geographic collaborations. Results: AI research in Obs&Gyn increased notably after 2017. North America and Europe led in publication output, with Asia also showing strong contributions. Prominent themes included machine learning, deep learning, IVF, diagnostic imaging, and robotic surgery. Collaboration networks revealed strong institutional and international partnerships. Conclusion: This study underscores the transformative potential of AI in Obs&Gyn and highlights critical research areas, key contributors, and collaboration dynamics. Findings provide a foundation for future research, emphasizing the need for inclusivity, ethical AI adoption, and addressing global healthcare disparities in Obs&Gyn.