Li XR, Kong MW, Guan XF, Gao Y. Revolutionizing upper gastrointestinal disease diagnosis: The transformative role of artificial intelligence in endoscopy. World J Gastrointest Endosc 2025; 17(7): 108293 [DOI: 10.4253/wjge.v17.i7.108293]
Corresponding Author of This Article
Yu Gao, MD, Professor, Chief Physician, Department of Endocrinology, Hebei Key Laboratory of Panvascular Diseases, Affiliated Hospital of Chengde Medical University, No. 36 Nanyingzi Street, Shuangqiao District, Chengde 067000, Hebei Province, China. yugao815@163.com
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/
World J Gastrointest Endosc. Jul 16, 2025; 17(7): 108293 Published online Jul 16, 2025. doi: 10.4253/wjge.v17.i7.108293
Revolutionizing upper gastrointestinal disease diagnosis: The transformative role of artificial intelligence in endoscopy
Xin-Rui Li, Mo-Wei Kong, Xiang-Feng Guan, Yu Gao
Xin-Rui Li, Department of Cardiology, Guiqian International General Hospital, Guiyang 550018, Guizhou Province, China
Mo-Wei Kong, Xiang-Feng Guan, Department of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China
Yu Gao, Department of Endocrinology, Hebei Key Laboratory of Panvascular Diseases, Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei Province, China
Co-first authors: Xin-Rui Li and Mo-Wei Kong.
Author contributions: Li XR and Kong MW contribute equally to this study as co-first authors; Kong MW and Guan XF provided crucial suggestions and guidance for the writing; Li XR wrote the manuscript; Gao Y reviewed and revised the manuscript; all authors read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that no author has any conflict of interest.
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: Yu Gao, MD, Professor, Chief Physician, Department of Endocrinology, Hebei Key Laboratory of Panvascular Diseases, Affiliated Hospital of Chengde Medical University, No. 36 Nanyingzi Street, Shuangqiao District, Chengde 067000, Hebei Province, China. yugao815@163.com
Received: April 10, 2025 Revised: April 16, 2025 Accepted: June 13, 2025 Published online: July 16, 2025 Processing time: 90 Days and 17.9 Hours
Abstract
With the rapid advancement of technology, artificial intelligence (AI) has emerged as a transformative force in gastroenterology, particularly in diagnosing upper gastrointestinal diseases such as Barrett's esophagus (BE), esophageal cancer, gastroesophageal reflux disease (GERD), and esophagogastric varices. AI's capabilities in image analysis, classification, detection, and segmentation have significantly improved diagnostic accuracy and efficiency. For BE, AI models achieve high sensitivity and specificity in detecting early neoplastic changes and guiding targeted biopsies. In esophageal cancer, AI enhances early lesion detection, improving intervention success rates. For GERD, AI classifies disease severity based on the Los Angeles grading system and accurately segments lesions. Additionally, AI detects esophagogastric varices and predicts bleeding risks more effectively than traditional methods. Despite these advancements, challenges remain, including the need for high-quality data, multi-center validation, and ensuring AI model interpretability. Future research should address these issues and further integrate AI into clinical practice to optimize patient outcomes. This review highlights AI's transformative impact on upper gastrointestinal disease diagnosis, emphasizing its potential to revolutionize endoscopic practice and improve patient care.
Core Tip: This review highlights the application progress of artificial intelligence in the diagnosis of upper gastrointestinal diseases, emphasizing its potential in improving the accuracy of endoscopic image analysis, early lesion detection, and disease grading. It also addresses the challenges faced in clinical implementation, including data quality, multi-center validation, and model interpretability.
