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Artif Intell Cancer. Sep 8, 2026; 7(1): 119655
Published online Sep 8, 2026. doi: 10.35713/aic.119655
Published online Sep 8, 2026. doi: 10.35713/aic.119655
Artificial intelligence in liver disease: Current status and future direction
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, Farming
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
Revised: February 20, 2026
Accepted: April 7, 2026
Published online: September 8, 2026
Processing time: 212 Days and 4.7 Hours
Core Tip
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.