Artificial intelligence in shoulder and elbow surgery: a bibliometric analysis of affiliation-based collaboration patterns

Abstract

Background: Despite growing interest, artificial intelligence (AI) applications in shoulder and elbow surgery remain underdeveloped. While adoption is accelerating and shows promise in addressing complex clinical problems, substantial technical and clinical barriers persist. Collaborative research may be relevant for generating high-quality datasets and more robust, generalizable, and clinically relevant algorithms. This study aimed to 1) analyze trends in AI research productivity and impact, 2) map collaboration patterns among affiliations and regions, and 3) assess the relationship between affiliation-based collaboration and research outcomes. Methods: We conducted a bibliometric analysis of Scopus-indexed articles published between January 2000 and November 2024, focusing on peer-reviewed studies involving AI applications in shoulder or elbow surgery. Data collected included number of publications, citation metrics, author affiliations, and index keywords. These variables were used to calculate composite metrics and to examine the geographic distribution of research and collaboration patterns using network analysis. Two linear regression models assessed the relationship between affiliation-based collaborations and publication volume and citation impact. Results: = 0.77). Conclusion: Affiliation-based collaboration was strongly associated with both the volume and citation impact of AI research in shoulder and elbow surgery. Strengthening and expanding these networks may enhance global research participation, foster innovation, and improve the clinical applicability of future work.

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