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World J Gastrointest Endosc. Apr 16, 2026; 18(4): 117976
Published online Apr 16, 2026. doi: 10.4253/wjge.v18.i4.117976
Published online Apr 16, 2026. doi: 10.4253/wjge.v18.i4.117976
Artificial intelligence in endoscopic ultrasound: Clinical translation of a prediction, navigation, and diagnosis framework
Zhi-Yuan Chen, Yao-Qi Wang, Peng Liu, Ya Peng, Department of Gastroenterology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410005, Hunan Province, China
Xian-Zheng Tan, Department of Radiology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410005, Hunan Province, China
Author contributions: Chen ZY and Wang YQ contributed to the conceptualization, literature search, and drafting of the original manuscript; Tan XZ was responsible for data curation and visualization; Liu P contributed to the methodology and critical revision of the manuscript; Peng Y supervised the project, provided critical intellectual input, and finalized the manuscript; and all authors have read and approved the final version of the manuscript.
Supported by the General Program of Hunan Provincial Natural Science Foundation, No. 2025JJ50695.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Ya Peng, MD, Chief Physician, Department of Gastroenterology, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, No. 61 Jiefang West Road, Furong District, Changsha 410005, Hunan Province, China.
Received: December 22, 2025
Revised: January 11, 2026
Accepted: March 3, 2026
Published online: April 16, 2026
Processing time: 114 Days and 17.4 Hours
Revised: January 11, 2026
Accepted: March 3, 2026
Published online: April 16, 2026
Processing time: 114 Days and 17.4 Hours
Core Tip
Core Tip: This article proposes an artificial intelligence-powered “Prediction-Navigation-Diagnosis” framework for endoscopic ultrasound. It highlights how artificial intelligence improves preoperative risk stratification, reduces intraoperative missed detections (about 10%) via real-time navigation, and enhances diagnostic accuracy. Key challenges for clinical adoption - data heterogeneity, high costs, and unclear regulations - are summarized, with future success hinging on multimodal integration and adaptive policies.