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

Integrating artificial intelligence (AI) has the potential to significantly advance digital twin (DT) technologies by enhancing data analytics, predictive modeling, and real-time decision-making capabilities. The present scientometric review examines the intersection of AI and DT technologies, utilizing a comprehensive dataset sourced from Scopus. Initially, 3526 records were identified and refined to 1302 through specific filters. For detailed analysis, the records were categorized into four subcategories: 1) architecture; 2) applications; 3) DT as middleware; and 4) tools. Using advanced scientometric tools, namely, CiteSpace and VOSviewer, the research conducted five distinct analyses: 1) publication pattern; 2) keyword co-occurrence; 3) prominent journals; 4) document co-citation; and 5) author co-citation analysis. The findings reveal significant trends and patterns in the literature, highlighting the evolving landscape of AI-integrated DTs. 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.