Application of artificial intelligence in gynecologic cancers: A bibliometric analysis

This study aimed to systematically characterize the landscape of artificial intelligence (AI) applications in gynecologic cancers, offering a comprehensive overview of current research trends, influential publications, key contributors, and future research directions. The focus of this study was to provide a quantitative overview of the field's development and trends.

A structured search was performed in the Web of Science Core Collection to identify original articles on AI use in gynecologic oncology. Two independent reviewers screened and selected studies based on predefined inclusion criteria. Extracted data-including publication trends, author and institutional collaborations, keyword co-occurrence, and citation networks-were analyzed using CiteSpace 6.2.R6 and VOSviewer software.

A total of 2544 articles were included for analysis. Research activity showed a notable acceleration after 2019, reaching its highest output in 2024. China and the United States emerged as dominant contributors, with the Chinese Academy of Sciences and Fudan University leading among institutions. Influential authors such as Sala Evis, Tian Jie, and Scambia Giovanni were identified. Major research themes focused on "Radiomics," "Deep Learning," "Radiotherapy," and cancers including cervical, ovarian, and endometrial. Recent emerging topics included "Digital Pathology," "Personalized Medicine," and "Tumor Heterogeneity," signaling a shift toward precision oncology.

This bibliometric study delineated the evolving field of AI in gynecologic oncology, highlighting dynamic research fronts and gaps.