©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 28, 2025; 31(36): 110742
Published online Sep 28, 2025. doi: 10.3748/wjg.v31.i36.110742
Published online Sep 28, 2025. doi: 10.3748/wjg.v31.i36.110742
Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis
Shu-Qi Ren, Department of Laboratory Medicine, Zhongshan City Hospital of Integration of TCM & Western Medicine, Zhongshan 528467, Guangdong Province, China
Jin-Man Chen, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong Province, China
Chuang Cai, Cancer Research Institute of Zhongshan City, Zhongshan City People's Hospital, Zhongshan 528445, Guangdong Province, China
Author contributions: Ren SQ, Chen JM, and Cai C made substantial contributions to this manuscript; Ren SQ conceived the review and drafted the initial manuscript; Ren SQ and Chen JM were responsible for literature collation; Ren SQ and Cai C edited and finalized the manuscript for submission; All authors reviewed and approved the submitted manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Chuang Cai, PhD, Assistant Professor, Cancer Research Institute of Zhongshan City, Zhongshan City People's Hospital, No. 2 Sunwen East Road, Zhongshan 528445, Guangdong Province, China. caich6@foxmail.com
Received: June 16, 2025
Revised: July 4, 2025
Accepted: August 22, 2025
Published online: September 28, 2025
Processing time: 97 Days and 23.4 Hours
Revised: July 4, 2025
Accepted: August 22, 2025
Published online: September 28, 2025
Processing time: 97 Days and 23.4 Hours
Core Tip
Core Tip: Artificial intelligence (AI) demonstrates transformative potential across gastrointestinal and hepatic disorders. It enhances early detection of subtle lesions (e.g., Barrett's esophagus) by analyzing diverse clinical data, optimizes treatment decisions (e.g., therapy response in liver cancer) via integrated clinical data assessment (including multimodal integration where applicable), and refines prognostic prediction (e.g., recurrence risk in liver cancer). This translational AI enables intelligent clinical decision-making for diagnosis, personalized treatment, and prognosis assessment throughout the patient journey.