Bibliometrics of artificial intelligence applications in hepatobiliary surgery from 2014 to 2024
Ru-Jun Zheng1, Dong-Lun Li2, Hao-Min Lin3
1Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China.
Background:
In recent years, the rapid development of artificial intelligence (AI) in hepatobiliary surgery research has led to an increase in articles exploring its benefits. We performed a bibliometric analysis of AI applications in hepatobiliary surgery to better delineate the contemporary state of AI application in hepatobiliary surgery and potential future trajectories.
Aim:
To provide clinical practitioners with a reliable reference point. It offers a detailed overview of the development of AI in hepatobiliary surgery by systematically examining the contributions of authors, countries, institutions, journals, and keywords in this domain over the last 10 years.
Methods:
The academic resources utilized in this study were obtained from the Web of Science Core Collection database. The search results were subsequently integrated and imported into CiteSpace and VOSviewer software for the purpose of visual analysis.
Results:
The study analyzed 2552 publications during 2014-2024. These publications collectively garnered 32 628 citations, averaging 15.66 citations per paper. The top contributor to this field was China. The USA had the highest citation count. The author with the highest citation count was Summers RM. In terms of the number of articles published, the leading journals were Medical Physics. Excluding the subject search terms, the most frequently used keywords included "classification", "CT and "diagnosis".
Conclusion:
This bibliometric analysis indicates that research on AI in hepatobiliary surgery has entered a period of rapid development, particularly in the domain of disease imaging diagnostics.
