Publication Name	World Journal of Gastroenterology
Manuscript ID	122422
DOI	10.3748/wjg.122422
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Study
Article Title	Retrieval-augmented generation models for predicting pathological upgrading in endoscopically resected gastric low-grade intraepithelial neoplasia: A dual-center evaluation
Manuscript Source	Unsolicited Manuscript
All Author List	Qian Zhuang, Jin-Nian Cheng, Yu-Lu Zheng, Shan Wu, Yi-Min Chu, Lu Zhou, Jie Xia, Min Ning, Hai-Xia Peng, Xin-Jian Wan and Zhi-Xia Dong
Funding Agency and Grant Number	Funding Agency Grant Number the Shanghai Municipal Health Commission Health Industry Clinical Research Project of China 20234Y0016 the National Key Research and Development Program of China 2023YFC2413803 the National Key Research and Development Program of China 2023YFC2413806 the Shanghai Municipal Health Commission Smart Healthcare Special Research Project of China 2025ZHYL021 the Emerging Frontier Project of the Shenkang Development Center, China SHDC12024124 the Key Discipline Project of the Shanghai Municipal Health System, China 2024ZDXK0004
Corresponding Author	Zhi-Xia Dong, Chief Physician, MD, Professor, Digestive Endoscopic Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai 200233, Please enter the state or province, China. dzhixia2013@163.com
Key Words	Large language model; Retrieval-augmented generation; Gastric low-grade intraepithelial neoplasia; Pathological upgrading; Predictive modeling; Endoscopic submucosal dissection; Artificial intelligence; Clinical decision support
Core Tip	This study evaluated a novel retrieval-augmented generation (RAG) -enhanced large language model framework using dual-center data to predict pathological upgrading in gastric low-grade intraepithelial neoplasia. The RAG-enhanced DeepSeek-V3 and Qwen3-235b models achieved areas under the curve of 0.873 [95% confidence interval (95%CI): 0.828-0.901] and 0.862 (95%CI: 0.818-0.893), respectively, demonstrating performance comparable to experienced endoscopists and confirming their potential clinical utility.
Citation	Zhuang Q, Cheng JN, Zheng YL, Wu S, Chu YM, Zhou L, Xia J, Ning M, Peng HX, Wan XJ, Dong ZX. Retrieval-augmented generation models for predicting pathological upgrading in endoscopically resected gastric low-grade intraepithelial neoplasia: A dual-center evaluation. World J Gastroenterol 2026; In press DOI: https://doi.org/10.3748/wjg.122422
PDF	122422-in-press.pdf

Received	2026-04-20 08:00
Peer-Review Started	2026-04-20 08:00
First Decision by Editorial Office Director	2026-05-26 08:34
Return for Revision	2026-05-26 08:34
Revised	2026-06-15 05:10
Publication Fee Transferred	2026-06-24 08:12
Second Decision by Editor	2026-06-30 02:41
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-06-30 08:17
Articles in Press	2026-06-30 08:17
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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Copyright	©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.
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