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World J Gastroenterol. Jan 7, 2027; 33(1): 122422
Published online Jan 7, 2027. doi: 10.3748/wjg.122422
Published online Jan 7, 2027. doi: 10.3748/wjg.122422
Retrieval-augmented generation models for predicting pathological upgrading in endoscopically resected gastric low-grade intraepithelial neoplasia: A dual-center evaluation
Qian Zhuang, Yu-Lu Zheng, Shan Wu, Jie Xia, Min Ning, Xin-Jian Wan, Zhi-Xia Dong, Digestive Endoscopic Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
Jin-Nian Cheng, Yi-Min Chu, Lu Zhou, Hai-Xia Peng, Digestive Endoscopic Center, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200336, China
Jin-Nian Cheng, Yi-Min Chu, Lu Zhou, Hai-Xia Peng, Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200336, China
Co-first authors: Qian Zhuang and Jin-Nian Cheng.
Co-corresponding authors: Xin-Jian Wan and Zhi-Xia Dong.
Author contributions: Zhuang Q and Cheng JN conceptualized and designed the study, drafted the manuscript and contributed equally as co-first authors; Zheng YL and Wu S reviewed the data analyses and contributed to the discussion; Xia J, Ning M, Zhou L, and Chu YM assisted in data collection; Zhuang Q performed the literature search and construction of the retrieval-augmented generation knowledge base; Wu S provided statistical expertise and editorial assistance; Peng HX, Wan XJ, and Dong ZX supervised the study; Wan XJ and Dong ZX contributed equally as co-corresponding authors; and all authors contributed substantially to the work and approved the final version for publication.
AI contribution statement: Portions of this manuscript were edited using AI tools (Kimi, https://www.kimi.com/) solely for language refinement. The authors carefully reviewed and verified all AI-assisted outputs and take full responsibility for the scientific content of the manuscript.
Supported by the Shanghai Municipal Health Commission Health Industry Clinical Research Project of China, No. 20234Y0016; the National Key Research and Development Program of China, No. 2023YFC2413803 and No. 2023YFC2413806; the Shanghai Municipal Health Commission Smart Healthcare Special Research Project of China, No. 2025ZHYL021; the Emerging Frontier Project of the Shenkang Development Center, China, No. SHDC12024124; and the Key Discipline Project of the Shanghai Municipal Health System, China, No. 2024ZDXK0004.
Institutional review board statement: This study was conducted following the Helsinki Declaration and was approved by the Ethics Committee of Shanghai Sixth People's Hospital [Approval No. 2026-KY-092(K)] and Ethics Committee of Shanghai Tongren Hospital (Tongren Lunshen K2026-031-01).
Informed consent statement: Patients were not required to provide informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to endoscopic submucosal dissection by written consent.
Conflict-of-interest statement: The authors declare no competing financial interests.
Data sharing statement: The datasets generated and analyzed during this study are not publicly available due to patient privacy regulations but are available from the corresponding author upon reasonable request.
Corresponding author: Zhi-Xia Dong, MD, Chief Physician, 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, China. dzhixia2013@163.com
Received: April 20, 2026
Revised: June 15, 2026
Accepted: June 30, 2026
Published online: January 7, 2027
Processing time: 205 Days and 14.3 Hours
Revised: June 15, 2026
Accepted: June 30, 2026
Published online: January 7, 2027
Processing time: 205 Days and 14.3 Hours
Core Tip
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, demon