Artificial intelligence applications in knee osteoarthritis research: A bibliometric and visualized analysis

Artificial intelligence (AI) has become a transformative tool in knee osteoarthritis (KOA) research, providing new opportunities for diagnosis, prognosis, disease monitoring, and personalized management. However, the development trajectory, collaboration patterns, and emerging hotspots of this field remain insufficiently mapped. This study aimed to provide a bibliometric and visualized analysis of AI applications in KOA research.

Publications on AI applications in KOA from 2004-October 2025 were retrieved from the Web of Science Core Collection. Bibliometric indicators and collaboration networks, intellectual structure, and thematic evolution were analyzed using Bibliometrix, VOSviewer, CiteSpace, and Bibliometric.com.

A total of 663 publications were included in the final analysis, comprising 582 articles, 2 early access articles, 3 proceedings papers, and 76 reviews. The annual growth rate was 28.03%, indicating rapid expansion of the field. China led in publication output, while the United States ranked first in total citations and average citations. International collaboration exhibited a tripolar structure dominated by North America, Europe, and Asia, although cross-regional integration remained limited. Osteoarthritis and Cartilage was the most productive and influential journal. Thematic evolution revealed a shift from algorithm-centered methodological development, including deep learning and radiomics, toward clinically oriented applications such as predictive modeling, imaging-based assessment, arthroplasty, and decision support.

AI-related KOA research has grown rapidly, evolving from methodological exploration toward clinical translation. Future studies should strengthen international collaboration, multicenter validation, and integration of AI into real-world KOA management.