WGO AI Guidelines
Year: 2026
Type: article
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.
Authors: Miguel M. Saraiva, António M.M.P. da Costa, Matheus F. de Carvalho, Alberto Espino, Amrita Sethi, Ana Zatarain, Andrew Metz, Arnoldo Riquelme, Arthur Kaffes, Aymen Almuhaidb, Belén Agudo, Carlos A. González, Grace E. Kim, Jose I. Vargas, Khoon S. Kok, Laurence Lovat, Luis C. Sabbagh, Marcos Lera, Mariano González, Mariano Villarroel, Mason Adams, Mathew Keegan, Payal Saxena, Tamas Gonda, Uzma D. Siddiqui, Vipul Aggarwal, Wojciech Marlicz, Adrian Canavesi, Adriana F. Silva, Ana Pérez-Gonzalez, Andrea Velasquez, Andrés Taullard, Baret Bercier, Chance Dickson, Gustavo Ejima, Erika B. Fortes, Hiram M.N. Filho, Ivan González, Joana Mota, João G. R. J. Sasso, Luiza B. de Oliveira, Maria J. Almeida, Mateus B. Boghossian, Naif Alrossais, Nicholas Mingos, Nicolas González, Pedro Cardoso, Eduardo G.H. de Moura, Jessica Widmer, Guilherme Macedo
Institutions: Hospital de São João, Hospital Universitario Puerta de Hierro Majadahonda, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Pontificia Universidad Católica de Chile, Columbia University Irving Medical Center, Hospital Clínico San Carlos, The Royal Melbourne Hospital, Royal Prince Alfred Hospital, King Faisal Specialist Hospital & Research Centre, University of Chicago, Royal Liverpool University Hospital, University College London Hospitals NHS Foundation Trust, University College London, Hospital Universitario Reina Sofía, Hospital Británico de Buenos Aires, University of South Alabama, Northern Beaches Hospital, NYU Langone Health, Pomeranian Medical University, Hospital de Clínicas, D’Or Institute for Research and Education, Hospital Israelita Albert Einstein
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7225655940