Bibliometric and visual analysis of artificial intelligence-related research in osteoarthritis: Trends and frontiers (2008-2025)

Clinical Snapshot

Key Findings

• Effect Size: Not applicable for bibliometric analysis

• Primary Outcome: 659 articles identified with increasing annual publication trend since 2019

• Nnt Or Sensitivity: Leading countries: USA, China, UK; Top institutions: Harvard University, Seoul National University, UC system

• Confidence Interval: Not reported

Clinical Application

High feasibility for guiding research priorities and identifying collaboration opportunities Relevant for Australian researchers and institutions to understand global trends and identify potential international collaborations in AI-osteoarthritis research Researchers, clinicians, and policymakers interested in AI applications in osteoarthritis

Abstract

Osteoarthritis (OA) causes joint pain, functional impairment, and significantly impacts patients' quality of life. Recent advancements in artificial intelligence (AI) technology have provided new opportunities for OA diagnosis, treatment, and management. This study aims to understand the knowledge structure and research frontiers of "AI and OA" using bibliometric methods. Literature on "AI and OA" from January 1, 2008, to March 31, 2025, was retrieved from the Web of Science Core Collection database. VOSviewer, CiteSpace, and the R package "bibliometrix" were used for analysis. The study included 659 articles from 59 countries. Since 2019, the number of scientific outputs related to OA and AI has increased annually. The United States, China, and the United Kingdom are the main contributing countries, with Harvard University, Seoul National University, and the University of California system as leading institutions. The "JOURNAL OF ARTHROPLASTY" and "OSTEOARTHRITIS AND CARTILAGE" are the most contributing journals. Among the 2404 authors, GUERMAZI A and ROEMER FW are the most influential. Thematic trend analysis shows that AI and machine learning are driving new trends in OA research, especially in diagnosis, treatment planning, and patient monitoring. This paper summarizes the bibliometric trends and development in "AI and OA" studies over 18 years, identifying the latest research frontiers and hot topics. It provides valuable references for related research and highlights the importance of interdisciplinary collaboration as research topics diversify.

References

1. 1.Ma, Y., Feng, K., Chen, Z., & Jin, Q. (2026). Bibliometric and visual analysis of artificial intelligence-related research in osteoarthritis: Trends and frontiers (2008-2025). Medicine, 105(15), e48265. https://doi.org/10.1097/MD.0000000000048265