Publication Name	Artificial Intelligence in Gastroenterology
Manuscript ID	107277
Country	China

Received	2025-03-20 10:57
Peer-Review Started	2025-03-20 10:57
To Make the First Decision
Return for Revision	2025-03-26 02:07
Revised	2025-04-08 04:50
Second Decision	2025-04-21 02:42
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-04-21 09:43
Articles in Press	2025-04-21 09:43
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-05-28 01:56
Publish the Manuscript Online	2025-06-06 09:35

ISSN	2644-3236 (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/
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Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Revolutionizing viral hepatitis management: Artificial intelligence-assisted diagnosis and personalized treatment
Manuscript Source	Invited Manuscript
All Author List	Mei-Ling Chen, Wen-Mao Li, Qing Liu, Yue Gu and Jun-Rong Wang
Funding Agency and Grant Number
Corresponding Author	Jun-Rong Wang, Department of Gynaecology and Obstetrics, China-Japan Union Hospital of Jilin University, 126 Xiantai Street, Changchun 130022, Jilin Province, China. junrongwang_2019@yeah.net
Key Words	Artificial intelligence; Viral hepatitis; Machine learning; Early diagnosis; Precision medicine
Core Tip	Artificial intelligence (AI) is transforming the diagnosis and management of viral hepatitis by enhancing early detection, optimizing treatment strategies, and supporting public health efforts. AI-driven radiology, histopathology, and machine learning models improve diagnostic accuracy, while AI-assisted drug discovery and precision medicine enable personalized treatment approaches. Despite its promise, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to facilitate widespread clinical adoption. The integration of AI into clinical workflows and its application in low-resource settings offer significant potential to reduce the global burden of viral hepatitis. Future integration of AI into national hepatitis screening programs and clinical guidelines may standardize precision care across diverse healthcare settings.
Publish Date	2025-06-06 09:35
Citation	<p>Chen ML, Li WM, Liu Q, Gu Y, Wang JR. Revolutionizing viral hepatitis management: Artificial intelligence-assisted diagnosis and personalized treatment. <i>Artif Intell Gastroenterol</i> 2025; 6(1): 107277</p>
URL	https://www.wjgnet.com/2644-3236/full/v6/i1/107277.htm
DOI	https://dx.doi.org/10.35712/aig.v6.i1.107277

Full Article (PDF)	AIG-6-107277-with-cover.pdf
Manuscript File	107277_Auto_Edited_075209.docx
Answering Reviewers	107277-answering-reviewers.pdf
Audio Core Tip	107277-audio.mp3
Conflict-of-Interest Disclosure Form	107277-conflict-of-interest-statement.pdf
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Non-Native Speakers of English Editing Certificate	107277-non-native-speakers.pdf
Peer-review Report	107277-peer-reviews.pdf
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Scientific Editor Work List	107277-scientific-editor-work-list.pdf
CrossCheck Report	107277-crosscheck-report.pdf