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Zhou NY, Luo Q, Liu W, Yuan XL, Jiang H, Hu B. Artificial intelligence in chronic atrophic gastritis: A comprehensive review. World J Gastrointest Endosc 2026; 18(11): 124991 [DOI: 10.4253/wjge.124991]
Nuo-Ya Zhou, Qi Luo, Wei Liu, Xiang-Lei Yuan, Huan Jiang, Bing Hu, Department of Gastroenterology and Hepatology/Medical Engineering Integration Laboratory of Digestive Endoscopy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Co-first authors: Nuo-Ya Zhou and Qi Luo.
Author contributions: Zhou NY and Luo Q conducted a comprehensive literature review and composed the manuscript, both authors made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper; Liu W contributed invaluable critical insights and carried out substantial revisions; Yuan XL and Jiang H contributed significantly to the literature review process; Hu B meticulously revised the manuscript; the final version of the manuscript underwent thorough review and approval by all authors.
AI contribution statement: During the preparation of this manuscript, we used AI-assisted tools solely for language polishing and grammatical refinement to improve the readability and fluency of the text, and to assist in checking consistency among the main text, Table 1, and the reference list. Specifically, we used ChatGPT and DeepSeek for these purposes. All AI-generated suggestions were critically reviewed, revised as necessary, and verified by the authors against the original sources to ensure scientific accuracy, logical coherence, and conformity to the journal’s stylistic requirements. AI tools were not used for data analysis, interpretation, result generation, or any core scientific decision-making process. The authors assume full responsibility for the final content of this manuscript and its scientific integrity.
Supported by Natural Science Foundation of Sichuan Province, No. 2026YFHZ0074.
Conflict-of-interest statement: All authors have no conflict of interest to disclose.
Corresponding author: Bing Hu, Department of Gastroenterology and Hepatology/Medical Engineering Integration Laboratory of Digestive Endoscopy, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, China. hubing@wchscu.edu.cn
Received: June 29, 2026 Revised: September 1, 2026 Accepted: October 20, 2026 Published online: November 16, 2026 Processing time: 138 Days and 23.7 Hours
Abstract
Chronic atrophic gastritis (CAG) is a significant precancerous condition associated with gastric cancer, and its accurate diagnosis and comprehensive management are of great importance for reducing the risk of gastric cancer. In recent years, artificial intelligence (AI) has advanced rapidly in the field of digestive diseases, offering new approaches for the intelligent diagnosis and treatment of CAG. Early studies suggest that AI may improve diagnostic performance and assist in risk stratification. However, whether these systems reduce clinician workload or improve patient outcomes remains to be established. Most evidence comes from retrospective diagnostic accuracy studies, and no AI system has yet been incorporated into clinical guidelines. This review summarizes the research progress of AI in CAG, focusing on five key aspects: Assisted diagnosis, pathological interpretation, Helicobacter pylori infection diagnosis, risk prediction, and patient management. It aims to provide a reference for research on CAG and to highlight priorities for future research and clinical translation.
Core Tip: Chronic atrophic gastritis (CAG) is a key precancerous condition associated with gastric cancer, necessitating accurate diagnosis and effective management. Artificial intelligence (AI) has emerged as a promising tool in digestive diseases, offering novel solutions for intelligent CAG care. Early evidence suggests that AI may improve diagnostic accuracy and assist in risk stratification. However, its impact on clinician workload and patient outcomes remains to be demonstrated, and no AI system described here is currently ready for guideline-based clinical use. This review synthesizes AI research in CAG across five domains: Assisted diagnosis, pathological interpretation, Helicobacter pylori infection diagnosis, risk prediction and patient management, with the aim of informing future research and clinical translation.
