Zhang CY, Yang M. Artificial intelligence in liver disease: Current status and future direction. Artif Intell Cancer 2026; 7(1): 119655 [DOI: 10.35713/aic.119655]
Corresponding Author of This Article
Ming Yang, PhD, Assistant Professor, Department of Surgery, School of Medicine, University of Connecticut, 263 Farmington Avenue, Farmington, CT 06030, United States. minyang@uchc.edu
Research Domain of This Article
Computer Science, Artificial Intelligence
Article-Type of This Article
review-article
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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/
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Zhang CY, Yang M. Artificial intelligence in liver disease: Current status and future direction. Artif Intell Cancer 2026; 7(1): 119655 [DOI: 10.35713/aic.119655]
Artificial intelligence in liver disease: Current status and future direction
Chun-Ye Zhang, Ming Yang
Chun-Ye Zhang, Bond Life Sciences Center, University of Missouri, Columbia, MO 65212, United States
Ming Yang, Department of Surgery, School of Medicine, University of Connecticut, Farmington, CT 06030, United States
Author contributions: Zhang CY and Yang M designed the study and drafted, revised, and finalized the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Ming Yang, PhD, Assistant Professor, Department of Surgery, School of Medicine, University of Connecticut, 263 Farmington Avenue, Farmington, CT 06030, United States. minyang@uchc.edu
Received: February 3, 2026 Revised: February 20, 2026 Accepted: April 7, 2026 Published online: September 8, 2026 Processing time: 212 Days and 4.7 Hours
Abstract
Chronic liver disease is a leading cause of death globally, primarily owing to liver cirrhosis and hepatocellular carcinoma. Early diagnosis and effective treatment are critical for curative therapy. By integrating imaging data, multiomics data, clinical test results, and electronic health records, artificial intelligence (AI) and machine learning algorithms are increasingly being developed to improve the diagnosis, prognosis, and treatment-related decision-making of liver disease. Notable AI models include CatBoost, ALADDIN (mAchine Learning ADvanceD fibrosis and rIsk MASH Novel predictor), GigaTIME, and METABOLISM. Additionally, AI supports donor organ quality assessment, outcome prediction, and the optimization of drug delivery and therapeutic efficacy. This mini-review summarizes the evidence of AI applications in enhancing liver disease diagnosis, prognosis, treatment, and research. However, many AI models remain difficult to interpret or explain and face challenges such as data bias and limited generalizability across regions. Furthermore, clinical integration of AI requires robust multicenter validation, data interpretability, ethical compliance, and adequate healthcare infrastructure.
Core Tip: Chronic liver disease is a leading cause of disease-related mortality worldwide. Artificial intelligence, including machine learning and deep learning algorithms, is increasingly being applied to the diagnosis, prognosis, and prediction of treatment outcomes in chronic liver disease, to prevent progression to cirrhosis and hepatocellular carcinoma. Although limitations exist, integrating artificial intelligence into clinical workflows can help reduce errors and facilitate the extraction of critical information from large electronic health record datasets and complex diagnostic images.
