Bibliographic Analysis of Artificial Intelligence-Assisted Publications Used in Abdominal CT Imaging in the Last 10 Years

Abstract

Aim: This study presents a bibliometric analysis of artificial intelligence (AI)-)-assisted publications in abdominal computed tomography (CT) over the past decade. By examining publication trends, citation patterns, and research collaborations, this study offers insights into the evolving impact of AI in abdominal imaging. Materials and Methods: Data were retrieved from the Web of Science Core Collection using specific search criteria for 2014–2024. Bibliometric analysis was conducted using VOSviewer to generate co-occurrence networks, citation maps, and collaboration patterns. The study included keyword analysis, co-authorship analysis, co-citation analysis, and bibliographic coupling. Results: A significant increase in AI-related publications in abdominal CT has been observed in recent years, with deep learning emerging as the dominant methodology. Citation network analysis identified key studies focused on image reconstruction, segmentation, and radiomics. Collaboration networks highlighted strong international and inter-institutional partnerships, particularly among institutions in the United States, China, and South Korea. Additionally, industry-academic collaborations, notably with GE Healthcare, have contributed to the advancement of AI in abdominal imaging. Conclusions: AI-assisted abdominal CT imaging continues to expand as a critical area of research, demonstrating increasing interdisciplinary collaborations. Deep learning and radiomics have become focal points, influencing clinical decision support and quantitative imaging analysis. Future research should prioritize AI integration into routine radiology practice and explore its clinical effectiveness through large-scale validation studies.

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