Majeed AA, Butt AS. Leveraging artificial intelligence to differentiate benign from malignant biliary strictures: A step toward precision diagnosis. Artif Intell Gastroenterol 2026; 7(2): 118476 [DOI: 10.35712/aig.118476]
Corresponding Author of This Article
Amna Subhan Butt, Associate Professor, Department of Medicine, Aga Khan University Hospital, Stadium Road, Karachi 74800, Pakistan. amna.subhan@aku.edu
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
review-article
Open-Access Policy of This Article
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/
Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 118476 Published online Aug 8, 2026. doi: 10.35712/aig.118476
Leveraging artificial intelligence to differentiate benign from malignant biliary strictures: A step toward precision diagnosis
Ammara Abdul Majeed, Amna Subhan Butt
Ammara Abdul Majeed, Amna Subhan Butt, Department of Medicine, Aga Khan University Hospital, Karachi 74800, Pakistan
Author contributions: Majeed AA performed a literature search and wrote this review article; Butt AS received the invitation for a review article, developed an abstract and received approval, developed an outline, further reviewed the article for important intellectual content; and all authors have read and approved the final version of the manuscript to be published.
AI contribution statement: ChatGPT, Grammarly, DeepSeek were used to improve linguistics.
Conflict-of-interest statement: All authors declare that they have no conflict of interest to disclose.
Corresponding author: Amna Subhan Butt, Associate Professor, Department of Medicine, Aga Khan University Hospital, Stadium Road, Karachi 74800, Pakistan. amna.subhan@aku.edu
Received: January 4, 2026 Revised: March 3, 2026 Accepted: June 8, 2026 Published online: August 8, 2026 Processing time: 215 Days and 18.7 Hours
Abstract
Differentiating benign from malignant biliary strictures is a crucial decision-making step in the management of patients presenting with biliary strictures. Despite the availability of various modalities including abdominal imaging and endoscopic interventions, the overlapping features and technical procedural challenges attribute to the difficulty in confirming benign vs malignant strictures. These limitations and the high attributing cost of multimodality diagnostic workups often lead to delays in care delivery and impact prognosis. Artificial intelligence (AI) potentially appears promising in improving diagnostic precision via AI-driven algorithms, including deep learning and radiomics. This narrative review explores the role of AI in the accurate classification of biliary strictures to facilitate early detection of biliary cancer to optimize patient outcomes. Integration of AI into clinical workflows could revolutionize biliary stricture evaluation by offering a non-invasive, consistent, cost-effective and data-driven diagnostic pathway.
Core Tip: The current management of biliary stricture is highly dependent on multimodality diagnostic pathways to differentiate the benign or malignant nature of the disease process. Despite the availability of multimodality diagnostic tools, precision in diagnosis remains a high-stake clinical challenge. Various artificial intelligence (AI)-based approaches such as deep learning have emerged as potential tools to enhance diagnostic precision. From lesion characterization to assistance in obtaining cholangioscopy or endoscopic ultrasound-guided targeted biopsies, AI enhanced cross-sectional imaging analysis (radiomics) has a promising role. However, further evidence is required to overcome the limitations including insufficient outcome-driven validation, biases in study designs, and lack of interpretability. Hence, well designed trials, explainable AI systems, integration into existing diagnostic pathways and validation based on outcomes would help to enhance the accuracy and actual utility of AI in precision diagnosis of biliary strictures.
