Artificial intelligence in joint arthroplasty: A bibliometric analysis of global research trends (2001-2025).

BACKGROUND

Artificial intelligence (AI) has significantly advanced the field of joint arthroplasty by transforming key aspects such as surgical planning, implant design, and postoperative management. Despite their growing importance, research trends and priorities in AI applications for joint arthroplasty remain underexplored. This study employed bibliometric analysis to elucidate the main research focus areas and global trends in AI and arthroplasty from 2001 to 2025.

METHODS

Relevant publications were retrieved from the Web of Science Core Collection. Bibliometric and visualization tools, including CiteSpace, VOSviewer, and Scimago Graphica, were used to analyze the data. Key metrics, such as countries, institutions, authors, journals, references, and keywords, were examined to identify influential contributors and emerging research hotspots. This study did not require ethical approval from institutional review board.

RESULTS

A total of 533 publications were identified, demonstrating a steady increase in both publication volume and citations over time, with a peak of 136 publications by 2024. England emerged as the leading country in terms of research output, while Harvard University in the USA was identified as the most productive institution. The influential authors included Kwon Young-Min, Ramkumar Prem, and Mont Michael A. The Journal of Arthroplasty has led to the publication volume. Frequently occurring keywords included "machine learning," "AI," "total knee arthroplasty," "total hip arthroplasty," and "deep learning." Keyword burst analysis has revealed "implant identification" as a prominent recent research focus.

CONCLUSIONS

This bibliometric analysis highlighted the rapid growth and evolution of research priorities in AI applications for joint arthroplasty. With the increasing prevalence of advanced techniques, such as machine learning and deep learning, research in this area is expected to further revolutionize clinical practice. Future efforts should focus on optimizing AI-based solutions to address clinical challenges, improve patient outcomes, and foster international collaborations.