Aim: This study aims to perform a comprehensive bibliometric analysis of academic publications on AI applications in vascular surgery, identifying key authors, influential journals, prevalent research themes, and international collaborations, focusing on infrastructure, conceptual structure, and social networks within the field.
Material and Methods: The analysis covers 815 documents published from 2020 to 2024, retrieved from the Web of Science Core Collection database. Metrics analyzed include publication growth, citation rates, key contributors, leading journals, prevalent themes, and international collaborations.
Results: The research output showed a 15% annual growth rate, peaking in 2023. Despite increasing publications, the average citation rate per article declined. The study identified 5039 contributors with significant international co-authorship. Leading authors included Lareyre F and Raffort J, and the "Journal of Vascular Surgery" was the most influential journal. The USA and China led in contributions, reflecting robust research infrastructure. Key themes include risk assessment, diagnostic methods, and patient management, highlighting AI's role in enhancing diagnostic accuracy, treatment planning, and patient outcomes in vascular surgery.
Conclusion: The analysis highlights the rapid growth and collaborative nature of AI research in vascular surgery. Key contributors, influential journals, and emerging themes were identified, emphasizing AI's role in improving diagnostics and patient outcomes. Limitations include the focus on one database and a five-year period, suggesting future research should include more databases and a longer timeframe. Exploring high-impact studies and practical applications will further advance the field.
Key words: Artificial intelligence, vascular surgery, bibliometric analysis
|
