The Growing Role of Large Language Models in Arthroplasty Manuscripts: High-Productivity Authors Lead the Way.
cross_sectional · Level IV
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- Record sourced from PubMed, PMID 42501766.
- Also identified by DOI 10.1016/j.arth.2026.07.041.
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Abstract
The extent and characteristics of Large Language Model (LLM) utilization in arthroplasty literature remain undefined. In this study, we aimed to quantify the extent of LLM utilization in manuscripts across major arthroplasty journals. Additionally, we sought to assess temporal trends in LLM utilization, as well as associations with author productivity, geographic origin, and citation impact. A cross-sectional analysis of 3,352 original research articles from six arthroplasty journals from the era before advanced Large Language Models (LLMs) (Pre-AI) (2018 to 2022) and the era after advanced LLMs (post-AI) (2023 to 2025) was performed. The text was processed using a detection algorithm. Journal-specific thresholds for significant AI involvement were established (mean + two standard deviations of pre-AI scores). Author productivity, primary language of affiliate country, and citation counts were analyzed. In the post-AI era, five of six journals demonstrated significantly higher odds of AI involvement (P < 0.05). The proportion of AI-flagged articles rose from less than 4.2% (2018 to 2021) to 20.4% in 2025, which was a notable nonlinear increase compared to 2024. The first authors in the 90<sup>th</sup> percentile of the dataset for authorship demonstrated significantly greater odds of exceeding AI thresholds compared to authors who only had one publication in the dataset (odds ratio (OR) = 1.83, P < 0.001). Conversely, non-English affiliate country authors (P = 0.436) and zero-citation-count articles (P = 0.882) did not have higher odds. Unsurprisingly, detectable AI assistance in arthroplasty research has increased significantly since the public release of LLMs. The AI tools are disproportionately utilized by high-productivity authors but not non-English-speaking country authors, suggesting adoption is driven by research efficiency and scalability rather than language barriers. Higher rates of LLM use were not found in zero citation articles.