Chen ML, Li WM, Liu Q, Gu Y, Wang JR. Revolutionizing viral hepatitis management: Artificial intelligence-assisted diagnosis and personalized treatment. Artif Intell Gastroenterol 2025; 6(1): 107277 [DOI: 10.35712/aig.v6.i1.107277]
Corresponding Author of This Article
Jun-Rong Wang, Department of Gynaecology and Obstetrics, China-Japan Union Hospital of Jilin University, No. 126 Xiantai Street, Changchun 130022, Jilin Province, China. junrongwang_2019@yeah.net
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
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/
Artif Intell Gastroenterol. Jun 8, 2025; 6(1): 107277 Published online Jun 8, 2025. doi: 10.35712/aig.v6.i1.107277
Revolutionizing viral hepatitis management: Artificial intelligence-assisted diagnosis and personalized treatment
Mei-Ling Chen, Wen-Mao Li, Qing Liu, Yue Gu, Jun-Rong Wang
Mei-Ling Chen, School of Nursing, Jilin University, Changchun 130021, Jilin Province, China
Wen-Mao Li, Department of Rehabilitation, The Second Hospital of Jilin University, Changchun 130000, Jilin Province, China
Qing Liu, Department of Endocrinology and Metabolism, China-Japan Union Hospital of Jilin University, Changchun 130000, Jilin Province, China
Yue Gu, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Jun-Rong Wang, Department of Gynaecology and Obstetrics, China-Japan Union Hospital of Jilin University, Changchun 130022, Jilin Province, China
Co-corresponding authors: Yue Gu and Jun-Rong Wang.
Author contributions: Author contributions: Li WM contributed to the writing, editing of the manuscript and table; Wang JR, Gu Y contributed to the discussion and design of the manuscript; Liu Q contributed to the literature search; Wang JR, Chen ML designed the overall concept and outline of the manuscript. All authors have read and approve the final manuscript. Wang JR spearheaded the structural development and scholarly refinement of the manuscript. He orchestrated the critical revision process, ensuring methodological rigor and logical coherence across all sections. As co-corresponding author, he assumed responsibility for cross-team communication, addressing reviewers' technical inquiries, and finalizing the submission-ready version of the manuscript. Gu Y provided strategic leadership in shaping the manuscript's scientific narrative and theoretical framework. She designed the innovative conceptual architecture for the discussion section, integrating clinical implications with fundamental mechanistic insights. As co-corresponding author, Gu Y coordinated multi-institutional collaborations and supervised the translational interpretation of data. Her dual role encompassed both high-level academic mentorship and hands-on troubleshooting during the peer review process.
Conflict-of-interest statement: All the authors report having no relevant conflicts of interest for this article.
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: Jun-Rong Wang, Department of Gynaecology and Obstetrics, China-Japan Union Hospital of Jilin University, No. 126 Xiantai Street, Changchun 130022, Jilin Province, China. junrongwang_2019@yeah.net
Received: March 20, 2025 Revised: April 8, 2025 Accepted: April 21, 2025 Published online: June 8, 2025 Processing time: 79 Days and 5.6 Hours
Abstract
Viral hepatitis, including hepatitis B and hepatitis C (HCV), remains a significant global health burden, leading to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Traditional diagnostic methods, while effective, often face limitations in accuracy, accessibility, and timeliness. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, enhancing the detection, diagnosis, and treatment of viral hepatitis. This review explores the role of AI in viral hepatitis management, focusing on early detection through image analysis, digital pathology, and machine learning algorithms. AI-driven image analysis tools, such as convolutional neural networks, have demonstrated high accuracy in detecting HCV-related liver lesions from computed tomography scans. Supervised learning models such as support vector machines and hybrid quantum neural networks further enhance early risk stratification. AI also facilitates personalized treatment by predicting treatment responses, accelerating drug discovery, and advancing precision medicine. Furthermore, AI contributes to epidemiological surveillance by predicting disease spread and tracking treatment adherence. Despite its potential, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure equitable and effective AI implementation. Future directions include integrating AI into clinical workflows and expanding AI applications in low-resource settings. AI-assisted diagnosis and management have the potential to revolutionize viral hepatitis care, improving patient outcomes and reducing the global disease burden.
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.
