Utilizing Bibliometrics to Understand the Role of Machine Learning in the Current Orthopedic Arthroplasty Literature - Research Abstract

Aug 2, 2026

Upload by Noah

Machine learning is increasingly applied in orthopedic research, with arthroplasty studies benefiting from improved clinical and scientific problem solving. This study uses bibliometrics to map machine learning–focused arthroplasty publications from 1996 to 2023, aiming to identify existing research and forecast future hotspots. Publications are retrieved from Web of Science and analyzed with VOSviewer and Bibliometrix to reveal trends, contributing countries, institutions, authors, and thematic structures.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages4

Reading Time10 min

AccessFree to download

Background

Methods

Results

Conclusions

Wayne State University | Medical Student Research Symposium | School of Medicine | | --- | --- | | March 2024
Utilizing Bibliometrics to Understand the Role of Machine Learning in the Current Orthopedic Arthroplasty Literature
Matthew Corsi BS.
Wayne State University School of Medicine, Detroit, Michigan, USA, [gg2405@wayne.edu](gg2405@wayne.edu)
Fong Nham MD.
Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, Michigan, USA, [nhamfong@gmail.com](nhamfong@gmail.com)
Mouhanad El-Othmani MD
Department of Orthopaedic Surgery, Brown University, Providence, Rhode Island, USA, [mohannad.othmani@gmail.com](mohannad.othmani@gmail.com)
Follow this and additional works at: [https://digitalcommons.wayne.edu/som_srs](https://digitalcommons.wayne.edu/som_srs)
Part of the Medicine and Health Sciences Commons | | Recommended Citation Corsi BS., Matthew; Nham MD., Fong; and El-Othmani MD, Mouhanad, "Utilizing Bibliometrics to Understand the Role of Machine Learning in the Current Orthopedic Arthroplasty Literature" (2024) . Medical Student Research Symposium. 325. [https://digitalcommons.wayne.edu/som_srs/325](https://digitalcommons.wayne.edu/som_srs/325) This Research Abstract is brought to you for free and open access by the School of Medicine at DigitalCommons@WayneState. It has been accepted for inclusion in Medical Student Research Symposium by an authorized administrator of DigitalCommons@WayneState. Utilizing Bibliometrics to Understand the Role of Machine Learning in the Current Orthopedic Arthroplasty Literature Matthew Corsi BS 1., Fong Nham MD.2, Mouhanad El-Othmani MD.3 1Wayne State University School of Medicine, Detroit, Michigan, USA. 2Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, Michigan, USA 3Department of Orthopaedic Surgery, Brown University, Providence, Rhode Island, USA Background: Machine learning technology has been demonstrated to be a very useful tool in current orthopedic research. Furthermore, machine learning has shown to be quite impactful in the field ofarthroplasty solving many clinical and scientific problems, leading to greater utilization in retrospective studies. This current study aims to identify machine learning arthroplasty research and predict future hotspots. We hypothesize that the production of current scientific literature on machine learning will be produced by US-based national institutions andwill have exponentially grown in the past 5 years. Methods: Machine learning arthroplasty publications between 1996 and 2023 were identified using the Web of Science Core Collection of Clarivate Analytics. Then bibliometric indicators were obtained and imported for further analysis with VOSviewer and Bibliometreix to identify previous and ongoing trends within this field. Results: The bibliometric sourcing identified a total of 235 documents that were associated with machine learning applications to arthroplasty. 34 countries published articles on the topic and the United States demonstrated to be the largest contributor. The year 2022 had the highest number of publications produced in a year, totaling 66 articles. A total of 405 institutions across the world had published articles, the most relevant institutions with the highest production were Harvard University and Harvard Medical School with 41 and 34 articles produced respectively. Kwon YM was the most productive author while Haeberle HS and Ramkumar PN are the most impactful based on h-index. Co-occurrence visualization and thematic map identified niche and major themes within the literature. Conclusions: Machine learning in arthroplasty research continues to show an increasing trend since 2021 with contributions from authors and institutions globally. United States institutions and authors are the leading contributors to machine learning applications in arthroplasty research. This study identifies previous, current, an

FAQs

What is the goal of this study on machine learning in orthopedic arthroplasty?

The study identifies machine learning arthroplasty research and predicts future hotspots using bibliometric analysis.

How were relevant publications collected and analyzed?

Machine learning arthroplasty publications from 1996 to 2023 were identified in the Web of Science Core Collection, then bibliometric indicators were analyzed with VOSviewer and Bibliometrix.

What were the main results regarding publication volume and leading contributors?

The analysis found 235 related documents, with 34 publishing countries and the United States as the largest contributor. The year 2022 had the most publications, and top institutions included Harvard University and Harvard Medical School.

Aug 2, 2026

Upload by Noah