Abstract
ABSTRACTIntroductionA comprehensive analysis of artificial intelligence’s (AI) integration into neurosurgery is vital to identify research priorities, address gaps, and inform strategies for equitable innovation.ObjectiveTo conduct a bibliometric analysis of Scopus-indexed (2014-2023) original articles at the intersection of AI and neurosurgery.MethodA descriptive metric study was conducted on 91 original articles, employing productivity, impact, and collaboration indicators. SciVal facilitated data extraction, while VOSviewer 1.6.11 enabled the mapping of co-authorship networks and keyword co-occurrence. IBM SPSS Statistics 27 was used to determine correlations between variables of interest (Kendall’s rank correlation coefficient, statistically significant for p < 0.05).ResultsThe 91 articles accumulated 2,197 citations (24.1/article), reflecting rising productivity. Most highly cited works (2019–2023) were published in Q1 journals. Dominant neurosurgical areas included neuro-oncology (25.4%) and education (20.9%), with AI applications focused on diagnostic accuracy (20.9%) and predictive tools (17.6%). Citations correlated with author numbers (p = 0.007).World Neurosurgeryled in publications (Ndoc = 11), whileJAMA Network Openhad the highest citations/article (88.7). Author, institutional, and country productivity correlated strongly with citations (p < 0.001). Collaboration was universal (international: 29.7%, national: 53.8%, institutional: 16.5%).ConclusionsThe analyzed scientific output exhibited a marked quantitative growth trend and high citation rates, with a predominant focus on leveraging AI to enhance diagnostic accuracy, particularly in neuro-oncology. Publications were concentrated in specialized, high-impact journals and predominantly originated from authors and institutions in high-income, technologically advanced Northern Hemisphere countries, where scientific collaboration played a foundational role in driving research advancements.
Publisher
Cold Spring Harbor Laboratory