Artificial Intelligence in Digital Twin Technology: Scientometric Insights on Architecture, Applications, and Tools

Integrating artificial intelligence has the potential to significantly advance digital twin technologies by enhancing data analytics, predictive modeling, and real-time decision-making capabilities. The present scientometric review examines the intersection of artificial intelligence and digital twin technologies, utilizing a comprehensive dataset sourced from Scopus. Initially, 3,526 records were identified and refined to 1,302 through specific filters. For detailed analysis, the records were categorized into four subcategories: architecture, applications, digital twin as middleware, and tools. Using advanced scientometric tools, namely CiteSpace and VOSviewer, the research conducted five distinct analyses: publication pattern, keyword co-occurrence, prominent journals, document co-citation, and author co-citation analysis. The findings reveal significant trends and patterns in the literature, highlighting the evolving landscape of artificial intelligence-integrated digital twins. The study provides insights into the current state of research and identifies key areas for future exploration, contributing to a broader understanding of innovative technology.