The Influence of an Artificial Intelligence Large Language Model (ChatGPT) on Orthopaedic Scientific Publishing: A Bibliometric Analysis

Gabriel Ferraz Ferreira, Thomas Lorchan Lewis, Miguel Viana Pereira Filho, Fabricio Machado, Gustavo Gonçalves Arliani, Noel Oizerovici Foni, Roberto Zambeli, Caio Nery, Rodrigo Pagnano
2025 · doi:10.32388/usaktb.2
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Abstract

PURPOSE: This study aimed to assess bibliometric trends in orthopaedic research before and after the public release of ChatGPT. METHODS: A bibliometric analysis was conducted using PubMed data from January 2021 to March 2025, encompassing articles from ten high-impact orthopaedic journals. Trends in daily publication frequency, number of co-authors per article, sentence length, and lexical diversity were compared between pre- and post-ChatGPT periods. RESULTS: A total of 19,380 articles were analysed. The mean number of publications per day increased significantly from 9.76 ± 6.79 to 12.02 ± 7.83 (p < 0.001). This difference remained significant after adjusting for monthly variation (p < 0.001). The mean number of authors per article rose from 5.9 ± 3.88 to 6.18 ± 4.04 (p < 0.001). Abstracts became slightly more concise, with the average sentence length decreasing from 14.95 ± 5.13 to 14.67 ± 5.04 (p < 0.001), while lexical diversity increased marginally (TTR: 0.5192 to 0.5233; p < 0.001). CONCLUSION: Since the introduction of ChatGPT, orthopaedic publications have shown a measurable rise in daily output, enhanced collaborative authorship, and subtle changes in linguistic style. These findings suggest a potential influence of AI-assisted tools on the way scientific research is written and disseminated.
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