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Articles Published Processes
12/10/2025 7:48:09 AM | Browse: 3 | Download: 6
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Received |
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2025-06-30 09:27 |
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Peer-Review Started |
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2025-06-30 09:27 |
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First Decision by Editorial Office Director |
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2025-08-06 11:45 |
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Return for Revision |
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2025-08-06 11:45 |
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Revised |
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2025-08-31 08:28 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-10-21 02:53 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-10-21 06:41 |
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Articles in Press |
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2025-10-21 06:41 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-10-30 10:32 |
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Publish the Manuscript Online |
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2025-12-10 07:48 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| 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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Review |
| Article Title |
Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Jing-Ran Sun, Xiao-Ning Sun, Bing-Jiu Lu and Bao-Cheng Deng |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Science Planning Project of Liaoning Province |
2019JH2/10300031-05 |
| National Natural Science Foundation of China |
12171074 |
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| Corresponding Author |
Bao-Cheng Deng, PhD, The Second Department of Infectious Diseases, The First Affiliated Hospital, China Medical University, No. 155 Nanjing North Street, Shenyang 110001, Liaoning Province, China. sydengbc@163.com |
| Key Words |
Artificial intelligence; Hepatology; Liver disease diagnosis; Deep learning; Clinical prediction models |
| Core Tip |
This review highlights how artificial intelligence is transforming hepatology by enabling early diagnosis, fibrosis staging, hepatocellular carcinoma detection, and personalized treatment. Key innovations include deep learning for imaging, multi-omics integration, and privacy-preserving federated learning. Explainable artificial intelligence builds clinician trust. Despite promising results, challenges like data heterogeneity, regulatory barriers, and limited real-time integration remain. Continued efforts in validation, ethical oversight, and user-centered design are essential for clinical adoption. |
| Publish Date |
2025-12-10 07:48 |
| Citation |
Sun JR, Sun XN, Lu BJ, Deng BC. Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models. World J Gastroenterol 2025; 31(46): 111176 |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i46/111176.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i46.111176 |
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