WGO AI Guidelines

Artificial intelligence is increasingly transforming gastrointestinal endoscopy, particularly in endoscopic ultrasound (EUS), biliary disease, and upper gastrointestinal (GI) endoscopy. EUS plays a key role in evaluating subepithelial lesions (SELs) and solid and cystic pancreatic neoplasms, incorporating techniques such as elastography and contrast enhancement. Nonetheless, its diagnostic performance remains strongly operator-dependent, with notable interobserver variability and only moderate accuracy even among experienced endosonographers. Computational models applied to EUS images and videos have shown encouraging results in lesion detection, characterization, and risk stratification, in some cases reaching or exceeding expert-level performance. However, most available data are retrospective and derived from single-center studies. Cholangiocarcinoma continues to be associated with poor outcomes, largely due to delayed diagnosis and the difficulty of distinguishing malignant from benign biliary strictures. Although advances in cholangioscopy, imaging, and tissue acquisition have improved diagnostic yield, indeterminate strictures remain frequent. The incorporation of artificial intelligence into imaging analysis, radiologic assessment, and tissue evaluation may improve diagnostic precision, decrease unnecessary procedures, and provide real-time decision support. In upper GI endoscopy, detection and characterization of esophageal and gastric neoplasia remain inconsistent in daily practice and are highly dependent on operator expertise. Emerging systems can enhance lesion recognition, reduce variability among observers, and assist real-time assessment. Overall, these technologies represent a promising adjunct across endoscopic modalities, although prospective multicenter validation and real-time clinical integration are still required for widespread adoption.

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Publication Details

Journal
Journal of Clinical Gastroenterology
Published
2026-11-09
DOI
https://doi.org/10.1097/mcg.0000000000002462
Primary Topic
AI in cancer detection
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article
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article

WGO AI Guidelines

Andrea Velasquez, Vipul Aggarwal, Mariano Villarroel, Wojciech M. Marlicz et al.
Journal of Clinical Gastroenterology
AI in cancer detection
article

WGO AI Guidelines

Andrea Velasquez, Vipul Aggarwal, Mariano Villarroel, Wojciech M. Marlicz, Eduardo Guimarães Hourneaux de Moura, Luiza Bicudo de Oliveira, Andrew J. Metz, Marcos Eduardo Lera, Mateus Bond Boghossian, Nicholas Mingos, Mathew James Keegan, Mason Adams, Laurence B. Lovat, Amrita Sethi, Andrés Taullard, Payal Saxena, Alberto Espino, Uzma D. Siddiqui, Tamas A. Gonda, Adrián Canavesi, Guilherme Macedo, Ana Zatarain, Gustavo Ejima, Arnoldo J. Riquelme, Baret Bercier, Joana Mota, Adriana Fernandes Silva, Nicolás Gónzalez, Miguel Mascarenhas Saraiva, Pedro Cardoso, Mariano González, Aymen Almuhaidb, Luis Carlos Sabbagh, Jessica L. Widmer, Erika Borges Fortes, Ana Pérez-Gonzalez, Jose Ignacio Vargas, Naif Alrossais, Khoon Sheng Kok, Belén Agudo, Maria J. Almeida, António M.M.P. da Costa, Carlos A. González, Grace E. Kim, Arthur Kaffes, Hiram M.N. Filho, Matheus F. de Carvalho, João G. R. J. Sasso, Chance Dickson, Ivan González
article en

Abstract

Artificial intelligence is increasingly transforming gastrointestinal endoscopy, particularly in endoscopic ultrasound (EUS), biliary disease, and upper gastrointestinal (GI) endoscopy. EUS plays a key role in evaluating subepithelial lesions (SELs) and solid and cystic pancreatic neoplasms, incorporating techniques such as elastography and contrast enhancement. Nonetheless, its diagnostic performance remains strongly operator-dependent, with notable interobserver variability and only moderate accuracy even among experienced endosonographers. Computational models applied to EUS images and videos have shown encouraging results in lesion detection, characterization, and risk stratification, in some cases reaching or exceeding expert-level performance. However, most available data are retrospective and derived from single-center studies. Cholangiocarcinoma continues to be associated with poor outcomes, largely due to delayed diagnosis and the difficulty of distinguishing malignant from benign biliary strictures. Although advances in cholangioscopy, imaging, and tissue acquisition have improved diagnostic yield, indeterminate strictures remain frequent. The incorporation of artificial intelligence into imaging analysis, radiologic assessment, and tissue evaluation may improve diagnostic precision, decrease unnecessary procedures, and provide real-time decision support. In upper GI endoscopy, detection and characterization of esophageal and gastric neoplasia remain inconsistent in daily practice and are highly dependent on operator expertise. Emerging systems can enhance lesion recognition, reduce variability among observers, and assist real-time assessment. Overall, these technologies represent a promising adjunct across endoscopic modalities, although prospective multicenter validation and real-time clinical integration are still required for widespread adoption.

Journal of Clinical Gastroenterology
The Royal Melbourne Hospital (AU), University College London Hospitals NHS Foundation Trust (GB), Pontificia Universidad Católica de Chile (CL), Columbia University Irving Medical Center (US), Royal Prince Alfred Hospital (AU), Hospital Israelita Albert Einstein (BR), King Faisal Specialist Hospital & Research Centre (SA), Royal Liverpool University Hospital (GB), Hospital Clínico San Carlos (ES), Hospital de São João (PT), University of Chicago (US), NYU Langone Health (US), D’Or Institute for Research and Education (BR), Hospital Universitario Puerta de Hierro Majadahonda (ES), Hospital Universitario Reina Sofía (ES), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (BR), Hospital Británico de Buenos Aires (AR), Hospital de Clínicas (UY), Northern Beaches Hospital (AU), University College London (GB), Pomeranian Medical University (PL), University of South Alabama (US)
AI in cancer detection
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