Scientometrics
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. May 27, 2025; 17(5): 104728
Published online May 27, 2025. doi: 10.4240/wjgs.v17.i5.104728
Bibliometrics of artificial intelligence applications in hepatobiliary surgery from 2014 to 2024
Ru-Jun Zheng, Dong-Lun Li, Hao-Min Lin, Jun-Feng Wang, Ya-Mei Luo, Yong Tang, Fan Li, Yue Hu, Song Su
Ru-Jun Zheng, Song Su, Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China
Dong-Lun Li, Department of Nephrology, University Hospital Essen, University of Duisburg-Essen, Essen 45147, Germany
Hao-Min Lin, Department of Hepatobiliary Pancreatic Surgery, Chengdu Sixth People’s Hospital, Chengdu 610000, Sichuan Province, China
Jun-Feng Wang, College of Computer Science, Sichuan University, Chengdu 610065, Sichuan Province, China
Ya-Mei Luo, School of Medical Information and Engineering, Southwest Medical University, Luzhou 646000, Sichuan Province, China
Yong Tang, School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu 611731, Sichuan Province, China
Fan Li, College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu 610225, Sichuan Province, China
Fan Li, The Institute of Digital Health and Medical ITA Innovation Industry, Chengdu 610225, Sichuan Province, China
Yue Hu, Department of Rehabilitation, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China
Yue Hu, Rehabilitation Medicine and Engineering Key Laboratory of Luzhou, Luzhou 646000, Sichuan Province, China
Song Su, Academician (Expert) Workstation of Sichuan Province, Metabolic Hepatobiliary and Pancreatic Diseases Key Laboratory of Luzhou City, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China
Co-first authors: Ru-Jun Zheng and Dong-Lun Li.
Co-corresponding authors: Yue Hu and Song Su.
Author contributions: Zheng RJ, Li DL, and Lin HM wrote the article; Li DL, Wang JF, Luo YM, and Hu Y analyzed the data and performed visualization; Hu Y performed preliminary revisions of the manuscript; Zheng RJ, Wang JF, Luo YM, Tang Y, and Li F checked the data and worked with the table; Su S revised the manuscript and supervised the study. All authors commented on previous versions of the manuscript. Zheng RJ and Li DL made similar and equal contributions in research design, obtaining and analyzing data from experiments, and writing the manuscript, so the two authors are listed as joint first authors. Hu Y and Su S made equal contributions in manuscript drafting, manuscript revision, and revision guidance, so the two authors are listed as joint corresponding authors.
Supported by the National Key Research and Development Program of China, No. 2023YFC3605202; Project of the Central Government in Guidance of Local Science and Technology Development, No. 2024ZYD0270; and Luzhou Science and Technology Bureau, No. 2024SYF156.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Song Su, MD, Professor, Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Luzhou 646000, Sichuan Province, China. 13882778554@163.com
Received: December 31, 2024
Revised: January 16, 2025
Accepted: February 18, 2025
Published online: May 27, 2025
Processing time: 144 Days and 5 Hours
Abstract
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.

Keywords: Artificial intelligence; Hepatobiliary surgery; Bibliometrics; Deep learning; Computed tomography

Core Tip: We performed a bibliometric analysis to better delineate the contemporary state of artificial intelligence (AI) application in hepatobiliary surgery, its evolution over recent years, and potential future trajectories. At the same time, we used visual maps to list in detail the current developmental trends and research status of AI, to provide practitioners with research hot spots and give a better understanding of the current situation in this field.