Artificial intelligence in hip and knee surgery: a bibliometric analysis of the 50 most cited articles

The integration of artificial intelligence (AI) into hip and knee surgery has been evolving rapidly, with significant implications for diagnostics, surgical planning, and outcome prediction. However, there has been limited literature with comprehensive overview of AI in arthroplasty surgery. This bibliometric analysis aims to identify the 50 most cited articles on AI in hip and knee surgery, highlighting key contributors, research trends, and methodological patterns.

We hypothesized that AI has generated a growing body of influential research in hip and knee surgery, with specific trends in applications, geographic distribution, and methodological approaches.

A systematic search was performed in the Web of Science Core Collection (WOSCC) on July 14, 2025, using predefined keywords related to AI and hip/knee surgery. Original research articles were screened and ranked by citation count. Descriptive statistics were used to analyze bibliometric variables including authorship, journal impact factor, country of origin, and AI techniques.

The 50 most cited articles, published between 2016 and 2023, accumulated a total of 7,140 citations (mean: 142.8; range: 59-735). The most cited article received 735 citations. The United States was the most prolific contributor, accounting for 27 articles (54.0%) and 2,772 citations (38.8%). Deep learning was the most frequently used AI technique (29 articles, 58% of articles). Knee-related topics were predominant, addressed in 32 articles (64.0%) while hip-related studies represented 18 articles (36.0%). Thematic focus was predominantly diagnostic with 31 articles (62.0%) centered on radiographic interpretation. There was no significant correlation between journal impact factor and citation count (Pearson's r = 0.21; p = 0.28).

This bibliometric analysis outlines the foundational literature driving AI adoption in hip and knee surgery. While the field is rapidly expanding, research remains unevenly distributed, with limited focus on hip surgery and treatment-oriented AI. Future studies should emphasize clinical validation, generalizability, and the integration of explainable AI into orthopedic practice.

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