Chen ZY, Wang YQ, Tan XZ, Liu P, Peng Y. Artificial intelligence in endoscopic ultrasound: Clinical translation of a prediction, navigation, and diagnosis framework. World J Gastrointest Endosc 2026; 18(4): 117976 [DOI: 10.4253/wjge.v18.i4.117976]
Corresponding Author of This Article
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. pengya123@hunnu.edu.cn
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Gastroenterology & Hepatology
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Minireviews
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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/
Apr 16, 2026 (publication date) through Apr 16, 2026
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Publication Name
World Journal of Gastrointestinal Endoscopy
ISSN
1948-5190
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Chen ZY, Wang YQ, Tan XZ, Liu P, Peng Y. Artificial intelligence in endoscopic ultrasound: Clinical translation of a prediction, navigation, and diagnosis framework. World J Gastrointest Endosc 2026; 18(4): 117976 [DOI: 10.4253/wjge.v18.i4.117976]
World J Gastrointest Endosc. Apr 16, 2026; 18(4): 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
Ya Peng, Peng Liu, Xian-Zheng Tan, Yao-Qi Wang, Zhi-Yuan Chen
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. pengya123@hunnu.edu.cn
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
Abstract
Endoscopic ultrasound (EUS) remains operator-dependent with notable diagnostic variability. This review synthesizes recent artificial intelligence (AI) advances within an integrated “Prediction-Navigation-Diagnosis” framework to transform EUS practice. Preoperatively, AI aids risk stratification and procedure planning. Intraoperatively, real-time navigation systems reduce anatomical blind-spot miss rates by approximately 10% and guide puncture paths. Postoperatively, AI enhances diagnostic accuracy for various gastrointestinal lesions, and cytology models alleviate reliance on scarce pathological resources. However, clinical adoption faces challenges including data heterogeneity, high costs, ethical ambiguities, and insufficient regulatory frameworks. Future translation depends on standardizing multimodal data, developing accessible algorithms, establishing human-AI collaboration guidelines, and advancing adaptive regulations. Overcoming these barriers may enable AI-enhanced EUS to achieve a more consistent, safe, and accessible intelligent workflow.
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.
