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World J Gastroenterol. Mar 28, 2026; 32(12): 115990
Published online Mar 28, 2026. doi: 10.3748/wjg.v32.i12.115990
Published online Mar 28, 2026. doi: 10.3748/wjg.v32.i12.115990
Artificial intelligence-assisted endoscopy in the detection of early gastrointestinal cancer: Progress, challenges, and future directions
Zhong-Xing Ning, Department of Hypertension and Vascular Disease, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
Zhong-Xing Ning, Department of Cardiovascular Medicine, Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nanning 530022, Guangxi Zhuang Autonomous Region, China
Zhong-Xing Ning, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
Jia-Jia Xiao, Guangxi Vocational and Technical College, Nanning 530022, Guangxi Zhuang Autonomous Region, China
Zi-Xiong Zhou, School of Economics and Management, Shanghai Institute of Technology, Shanghai 200235, China
Co-corresponding authors: Jia-Jia Xiao and Zi-Xiong Zhou.
Author contributions: Ning ZX, Xiao JJ and Zhou ZX contributed to the manuscript writing, reviewing, and editing; Ning ZX and Zhou ZX participated in the formal analysis, conceptualization, project administration, and supervision of this manuscript.
Supported by the School Level Project of Guangxi Vocational and Technical College, No. 231208.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Zi-Xiong Zhou, MD, Doctor, School of Economics and Management, Shanghai Institute of Technology, No. 120 Caobao Road, Xuhui District, Shanghai 200235, China. zozixoo@163.com
Received: October 31, 2025
Revised: November 29, 2025
Accepted: January 22, 2026
Published online: March 28, 2026
Processing time: 139 Days and 18.3 Hours
Revised: November 29, 2025
Accepted: January 22, 2026
Published online: March 28, 2026
Processing time: 139 Days and 18.3 Hours
Core Tip
Core Tip: Artificial intelligence (AI)-assisted endoscopy technologies have significantly advanced early detection and diagnosis of gastrointestinal cancers, enhancing adenoma detection rates and improving clinical outcomes. Recent studies demonstrate that AI, through deep learning models, can effectively identify small lesions, reduce missed diagnoses, and assist in clinical decision-making across various gastrointestinal regions, including the esophagus, stomach, and colon. However, challenges such as data quality, model generalization, and physician-AI collaboration remain. Overcoming these issues will ensure AI’s broader clinical integration, making it a vital tool in precision medicine and early cancer screening.