A bibliometric analysis of artificial intelligence applied to cervical cancer - Huang, Qiang; Su, Wenmei; Li, Shujun; Lin, Yanming; Cheng, Zhen; Chen, Yuting; Mo, Yanli (Frontiers in Medicine, 2025)
ObjectiveThis study conducts a bibliometric analysis of artificial intelligence (AI) applications in cervical cancer to provide a comprehensive overview of the research landscape and current advancements.MethodsRelevant publications on cervical cancer and AI were retrieved from the Web of Science Core Collection. Bibliometric analysis was performed using CiteSpace and VOSviewer to assess publication trends, authorship, country and institutional contributions, journal sources, and keyword co-occurrence patterns.ResultsFrom 1996 to 2024, our analysis of 770 publications on cervical cancer and AI showed a surge in research, with 86% published in the last 5 years. China (315 pubs, 32%) and the US (155 pubs, 16%) were the top contributors. Key institutions were the Chinese Academy of Sciences, Southern Medical University, and Huazhong University of Science and Technology. Research hotspots included disease prediction, image analysis, and machine learning in cervical cancer. Schiffman led in publications (12) and citations (207). China had the highest citations (3,819). Top journals were “Diagnostics,” “Scientific Reports,” and “Frontiers in Oncology.” Keywords like “machine learning” and “deep learning” indicated current research trends. This study maps the field's growth, highlighting key contributors and topics.ConclusionThis bibliometric analysis provides valuable insights into research trends and hotspots, guiding future studies and fostering collaboration to enhance AI applications in cervical cancer.