ReviewJSES international2026
Artificial intelligence in shoulder and elbow surgery: a bibliometric analysis of affiliation-based collaboration patterns.
Review in JSES international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial intelligence for postoperative plain radiographic assessment after reverse total shoulder arthroplasty: current evidence, clinical readiness, and limitations.Clinics in shoulder and elbow · 2026Article
- Global evolution of adolescent idiopathic scoliosis research (1990-2025): a bibliometric and visualization analysis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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: Of 181 identified scholarly documents, 119 met eligibility criteria. These articles were published across 63 journals and cited a total of 1,519 times. The 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.