Artificial Intelligence in Neurosurgery: A Bibliometric Analysis.

The review approach involved a systematic search of the Web of Science database. Investigators identified the fifty most frequently referenced papers concerning computational surgical support. This search spanned from the inception of the database through July 2022. The team extracted metadata including publication year, country of origin, and author gender. They performed statistical calculations using the R programming environment. This process allowed for the quantification of citation trends and demographic patterns. The researchers focused exclusively on high-impact literature to ensure relevance. This methodology provided a structured overview of the current academic landscape.

Key findings from the literature indicate that the top-cited paper was a systematic review from 2018. The citation counts for the analyzed list ranged from 29 to 159. The mean citation frequency reached 51.9 with a standard deviation of 24.8. Most of these influential works appeared after 2015, accounting for 85% of the total. The United States led the list with 22 contributing articles. Female first and last authorship occurred in only 18% and 0% of cases, respectively. Despite this, the impact of work by female authors matched that of their male colleagues. The data confirms a striking dominance of research originating from developed countries.

The authors suggest that the most cited papers demonstrate a clear concentration of research output within wealthy nations. This synthesis and implications framing indicates that global health equity remains a significant challenge for digital surgical tools. The researchers propose that female scholars produce work with impact levels comparable to their male counterparts. However, the data confirms a persistent gender gap in authorship representation. The review highlights that the majority of influential literature emerged after 2015. This observation suggests a rapid acceleration of interest in computational neurosurgical support. The team concludes that while these technologies show promise, current participation is not diverse. Future efforts should prioritize broader inclusion to ensure equitable development of these advanced systems.