69 Cross-border collaboration as a driver of surgical AI research success: A 25-year global analysis

Objectives/Goals: To identify drivers of publication impact and output in surgical AI across countries and assess whether cross-border collaboration, especially between developed and developing nations, is associated with higher citations and knowledge diffusion over 2000–2025. Methods/Study Population: We queried PubMed EDirect for surgical AI articles (2000–2025) and linked Crossref citations. Papers were grouped by collaboration type: single developed, single developing, developed–developed, developed–developing, and developing–developing. Success metrics included citations/paper, total papers, and collaboration rates. Country-level covariates from World Bank (infrastructure, economics, R&D, technology, education) were integrated. Analyses: chi-square(counts), Kruskal–Wallis (citation distributions), ANOVA (means), and Pearson correlations (predictors of citations and production). Results/Anticipated Results: Developed–developing collaborations had the highest citation impact (21.3 citations/paper) vs developed–developed (21.1), single developed (20.1), developing–developing (13.2), and single developing (8.7) (all p<0.001). In developing nations, citation impact correlated most with healthcare infrastructure – hospital beds (r=0.896), physicians/1,000 (r=0.891), and health expenditure (r=0.883) (all p<0.001). Paper production correlated most with economics and R&D—GDP (r=0.991), researchers in R&D (r=0.997), and R&D spend (r=0.989) (all p<0.001). Technology access (mobile subscriptions, r=0.874) and tertiary enrollment (r=0.871) also tracked with citation impact. Discussion/Significance of Impact: Cross-border collaboration, especially developed–developing, maximizes impact and advances equitable knowledge transfer in surgical AI. Policies should fund international partnerships, strengthen infrastructure in developing countries, and build networks to improve global diffusion of innovation.