Mapping the scientific landscape of navigated and robotic shoulder arthroplasty: A comprehensive bibliometric study

Navigation and robotic systems are increasingly applied to shoulder arthroplasty to improve accuracy and reproducibility of glenoid component placement. While early evidence is promising, the literature remains fragmented and scattered across subspecialties. Bibliometric analysis provides a means of mapping influential studies, contributors, and research trends.

A bibliometric search was conducted in May 2025 using Publish or Perish software (Version 8, Harzing.com) with Google Scholar as the data source. The search string ("shoulder arthroplasty") AND ("robot*" OR "robot-assisted" OR "computer assisted" OR navigation) identified 75 studies. The 50 most cited articles were included. Data extracted included publication and citation metrics, study design, country of origin, institutional and author contributions, journal distribution, and thematic keywords. Network visualisation was performed using Gephi.

The 50 most cited studies were published between 2005 and 2025, with a marked increase in output after 2014. Most articles originated from the USA and Europe, with a small group of authors and institutions disproportionately shaping the field. Navigation dominated keyword clusters, reflecting emphasis on implant accuracy and reproducibility. Clinical outcome data were limited, with most studies being small observational series. Robotics was rarely represented beyond feasibility reports.

Navigated and robotic shoulder arthroplasty is a rapidly growing but methodologically immature research field. Current evidence demonstrates improved technical accuracy with navigation, but robust outcome and cost-effectiveness data are lacking. Further multicentre, prospective studies are required to establish clinical value and guide adoption.