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Objective <p>To examine the global scientific production related to the application of artificial intelligence (AI) in orthopedics and traumatology through a bibliometric approach.</p> Methods <p>A search strategy was conducted in the Web of Science Core Collection, Scopus, and PubMed databases using terms associated with artificial intelligence and its application in orthopedics and traumatology. The publications were processed using the bibliometrix package in R to assess publication trends, citation impact, prolific authors, leading journals, collaborative patterns, and keyword trends.</p> Results <p>A total of 1405 scientific articles published in 433 journals on AI applied to orthopedics and traumatology were identified, with an annual growth rate of 20.66%. The year with the highest output was 2024 (400 articles), showing a greater increase from 2016 onwards. Prem N. Ramkumar was the most prolific author (29 articles), while Bradley J. Erickson stood out as the most cited (16,910 citations). The United States led in scientific production (418 articles) and total number of citations (8643), followed by China and Germany. The most productive journals were the Journal of Arthroplasty (57 articles) and Skeletal Radiology (41 articles); however, Acta Orthopaedica had the highest impact in terms of average citations per document. The most frequent keywords were “machine learning,” “deep learning,” “diagnosis,” and “algorithms,” with a shift toward specific clinical applications and diagnostic metrics in recent years. The most cited article was the study by Bini et al. (J Arthroplast 33:2358–2361, 2018).</p> Conclusion <p>AI research in orthopaedics and traumatology is growing rapidly, with expansion across imaging, prediction, outcome assessment, and other clinically oriented applications. Scientific output is geographically concentrated, led by the United States, and future progress will depend on stronger real-world validation, more diverse datasets, transparent reporting, and clinically meaningful outcome evaluation.</p>

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Global research trends and collaboration networks of artificial intelligence in orthopedics and traumatology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月10日

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  • Francesca A. Paz Gamarra,
  • Marko A. Llerena Castillo,
  • Guadalupe A. Pomasonco Olivas,
  • Fabriccio J. Visconti-Lopez

摘要

Objective

To examine the global scientific production related to the application of artificial intelligence (AI) in orthopedics and traumatology through a bibliometric approach.

Methods

A search strategy was conducted in the Web of Science Core Collection, Scopus, and PubMed databases using terms associated with artificial intelligence and its application in orthopedics and traumatology. The publications were processed using the bibliometrix package in R to assess publication trends, citation impact, prolific authors, leading journals, collaborative patterns, and keyword trends.

Results

A total of 1405 scientific articles published in 433 journals on AI applied to orthopedics and traumatology were identified, with an annual growth rate of 20.66%. The year with the highest output was 2024 (400 articles), showing a greater increase from 2016 onwards. Prem N. Ramkumar was the most prolific author (29 articles), while Bradley J. Erickson stood out as the most cited (16,910 citations). The United States led in scientific production (418 articles) and total number of citations (8643), followed by China and Germany. The most productive journals were the Journal of Arthroplasty (57 articles) and Skeletal Radiology (41 articles); however, Acta Orthopaedica had the highest impact in terms of average citations per document. The most frequent keywords were “machine learning,” “deep learning,” “diagnosis,” and “algorithms,” with a shift toward specific clinical applications and diagnostic metrics in recent years. The most cited article was the study by Bini et al. (J Arthroplast 33:2358–2361, 2018).

Conclusion

AI research in orthopaedics and traumatology is growing rapidly, with expansion across imaging, prediction, outcome assessment, and other clinically oriented applications. Scientific output is geographically concentrated, led by the United States, and future progress will depend on stronger real-world validation, more diverse datasets, transparent reporting, and clinically meaningful outcome evaluation.