Salman A, Elewa A, Marwan A, Salman MA. Artificial intelligence in endoscopic ultrasound-guided biliary drainage: Emerging applications, technical challenges, and future directions. World J Transl Med 2026; 12(2): 123702 [DOI: 10.5528/wjtm.123702]
Corresponding Author of This Article
Ahmed Salman, FRACP, FRCP, MRCP, Associate Professor, Department of Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Cairo 43544, Al Qāhirah, Egypt. awea844@gmail.com
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Gastroenterology & Hepatology
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review-article
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Salman A, Elewa A, Marwan A, Salman MA. Artificial intelligence in endoscopic ultrasound-guided biliary drainage: Emerging applications, technical challenges, and future directions. World J Transl Med 2026; 12(2): 123702 [DOI: 10.5528/wjtm.123702]
World J Transl Med. Jul 28, 2026; 12(2): 123702 Published online Jul 28, 2026. doi: 10.5528/wjtm.123702
Artificial intelligence in endoscopic ultrasound-guided biliary drainage: Emerging applications, technical challenges, and future directions
Ahmed Salman, Ahmed Elewa, Ahmad Marwan, Mohamed AbdAlla Salman
Ahmed Salman, Department of Internal Medicine, Kasr Alainy School of Medicine, Cairo 43544, Al Qāhirah, Egypt
Ahmed Elewa, Department of General Surgery, National Hepatology and Tropical Medicine Liver Institute, Cairo 16A, Egypt
Ahmad Marwan, Department of Internal Medicine, Faculty of Medicine, Mansoura University, Mansoura 3153, Egypt
Mohamed AbdAlla Salman, Department of General Surgery, Kasralainy School of Medicine, Cairo 11562, Egypt
Author contributions: Salman A contributed to the study conception, literature review, manuscript drafting, critical revision, and final approval of the manuscript; Elewa A contributed to manuscript revision, intellectual content review, and final approval of the manuscript; Marwan A contributed to manuscript drafting, critical revision, and final approval of the manuscript; Salman MA contributed to manuscript revision, intellectual content review, and final approval of the manuscript, made a substantial contribution to the literature review, interpretation of the evidence, and critical revision of the manuscript for important intellectual content.
AI contribution statement: Artificial intelligence tools, including ChatGPT and Grammarly, were used only for language refinement, grammar checking, and improving clarity during manuscript preparation and revision. All scientific content, interpretations, references, and final decisions were reviewed and approved by the authors. The authors take full responsibility for the integrity, accuracy, originality, and scientific validity of the manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Ahmed Salman, FRACP, FRCP, MRCP, Associate Professor, Department of Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Al-Manial, Cairo 43544, Al Qāhirah, Egypt. awea844@gmail.com
Received: May 27, 2026 Revised: June 20, 2026 Accepted: July 7, 2026 Published online: July 28, 2026 Processing time: 64 Days and 1.4 Hours
Abstract
Endoscopic ultrasound-guided biliary drainage (EUS-BD) has evolved from a rescue procedure following failed endoscopic retrograde cholangiopancreatography (ERCP) into an important therapeutic option for select patients with biliary obstruction. However, EUS-BD remains technically demanding and highly operator-dependent, requiring accurate anatomical recognition, safe route selection, stable access, and precise stent deployment. Artificial intelligence (AI) has the potential to improve the safety, reproducibility, and efficiency of EUS-BD by supporting procedural decision making. This review discusses emerging AI applications in EUS-BD, including the prediction of ERCP failure, pre-procedural imaging assessment, drainage route selection, real-time EUS image interpretation, bile duct recognition, vessel avoidance, needle trajectory planning, device tracking, stent deployment support, adverse event prediction, and post-procedural follow-up. AI can further assist in training and quality assurance through anatomical labeling, procedural feedback, and objective performance assessment. However, direct evidence for AI-guided EUS-BD remains limited, and most AI-EUS studies have focused on diagnostic rather than therapeutic applications. The major barriers include limited datasets, a lack of expert-labeled procedural videos, inadequate external validation, challenges in real-time integration, limited model explainability, and ethical and regulatory uncertainties. AI-assisted EUS-BD should therefore be considered as investigational rather than standard practice. Future progress requires multicenter validation studies and clinically meaningful evidence to demonstrate improved procedural performance and patient outcomes.
Core Tip: Artificial intelligence (AI) could become an important add-on to endoscopic ultrasound (EUS)-guided biliary drainage by supporting patient selection, pre-procedural planning, anatomical recognition, real-time procedural guidance, and adverse-event prediction. Although direct clinical evidence remains limited, AI has the potential to enhance procedural precision, reduce operator dependence, and improve training during this technically demanding procedure. Future adoption will necessitate multicenter datasets, prospective validation, explainable models, and seamless integration into the therapeutic EUS workflow.
