Zhou XQ, Huang S, Shi XM, Liu S, Zhang W, Shi L, Lv MH, Tang XW. Global trends in artificial intelligence applications in liver disease over seventeen years. World J Hepatol 2025; 17(3): 101721 [PMID: 40177211 DOI: 10.4254/wjh.v17.i3.101721]
Corresponding Author of This Article
Xiao-Wei Tang, PhD, Professor, Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Luzhou 646099, Sichuan Province, China. solitude5834@hotmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Scientometrics
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Hepatol. Mar 27, 2025; 17(3): 101721 Published online Mar 27, 2025. doi: 10.4254/wjh.v17.i3.101721
Global trends in artificial intelligence applications in liver disease over seventeen years
Xue-Qin Zhou, Shu Huang, Xia-Min Shi, Sha Liu, Wei Zhang, Lei Shi, Mu-Han Lv, Xiao-Wei Tang
Xue-Qin Zhou, Xia-Min Shi, Sha Liu, Wei Zhang, Lei Shi, Mu-Han Lv, Xiao-Wei Tang, Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
Shu Huang, Department of Gastroenterology, Lianshui People’ Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian 223499, Jiangsu Province, China
Co-first authors: Xue-Qin Zhou and Shu Huang.
Co-corresponding authors: Mu-Han Lv and Xiao-Wei Tang.
Author contributions: Tang XW and Zhou XQ contributed to study conception and design; Zhou XQ, Huang S, and Shi XM contributed to drafting of manuscript; Liu S and Zhang W contributed to acquisition of data and critical revision; Tang XW, Lü MH, Shi L contributed to revision of manuscript, and final approval of manuscript. All authors have read and approved the final manuscript.
Supported by Natural Science Foundation of Sichuan Province, China, No. 2022NSFSC1378.
Conflict-of-interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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: Xiao-Wei Tang, PhD, Professor, Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Luzhou 646099, Sichuan Province, China. solitude5834@hotmail.com
Received: September 24, 2024 Revised: January 1, 2025 Accepted: February 10, 2025 Published online: March 27, 2025 Processing time: 182 Days and 16.2 Hours
Abstract
BACKGROUND
In recent years, the utilization of artificial intelligence (AI) technology has gained prominence in the field of liver disease.
AIM
To analyzes AI research in the field of liver disease, summarizes the current research status and identifies hot spots.
METHODS
We searched the Web of Science Core Collection database for all articles and reviews on hepatopathy and AI. The time spans from January 2007 to August 2023. We included 4051 studies for further collection of information, including authors, countries, institutions, publication years, keywords and references. VOS viewer, CiteSpace, R 4.3.1 and Scimago Graphica were used to visualize the results.
RESULTS
A total of 4051 articles were analyzed. China was the leading contributor, with 1568 publications, while the United States had the most international collaborations. The most productive institutions and journals were the Chinese Academy of Sciences and Frontiers in Oncology. Keywords co-occurrence analysis can be roughly summarized into four clusters: Risk prediction, diagnosis, treatment and prognosis of liver diseases. "Machine learning", "deep learning", "convolutional neural network", "CT", and "microvascular infiltration" have been popular research topics in recent years.
CONCLUSION
AI is widely applied in the risk assessment, diagnosis, treatment, and prognosis of liver diseases, with a shift from invasive to noninvasive treatment approaches.
Core Tip: This study highlights the increasing annual publications on artificial intelligence in liver disease, with applications spanning risk assessment, diagnosis, treatment, and prognosis. China leads in publication output, whereas the United States remains a dominant force in the field. High-impact journals, authors, and institutions are identified, along with trends in international collaboration. Key research hotspots include "machine learning", "deep learning", "convolutional neural networks", "CT imaging", and "microvascular infiltration".
