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Review Article
Use of artificial intelligence in the management of cholangiocarcinoma: Current applications, evidence, and future perspectives
1 Internal Medicine Department, Faculty of Medicine, Cairo University, Cairo 11956, Egypt
2 General Surgery Department, National Hepatology and Tropical Medicine Research Institute, Cairo 11796, Egypt
3 Internal Medicine Department, Faculty of Medicine, Mansoura University, Mansoura 35511, Egypt
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Ahmed Salman
MD, Internal Medicine Department, Faculty of Medicine, Cairo University, Al-Saray Street, El Manial, Cairo 11956,
Egypt
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Article ID: 100111Z04AS2026
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Salman A, Elewa A, Marwan A. Use of artificial intelligence in the management of cholangiocarcinoma: Current applications, evidence, and future perspectives. Int J Hepatobiliary Pancreat Dis 2026;16(2):1–13.ABSTRACT
Cholangiocarcinoma is a highly aggressive and heterogeneous type of biliary cancer diagnosed late, with limited tissue availability for analysis, multiple staging challenges, and poor prognosis. In the case of ambiguous biliary strictures or in advanced stage disease, management should integrate endoscopic, radiological, pathological, molecular, and clinical information. In recent years artificial intelligence (AI) has been identified as an appropriate decision-support tool in this context. Deep learning approaches can support the recognition of malignant mucosal features during cholangioscopy and may assist endoscopists in targeting biopsies more accurately. In addition, radiomics and deep learning approaches can be used to diagnose the illness, classify tumors, assist in staging, estimate lymph node involvement, and assess recurrence risks with imaging methods including computed tomography (CT), magnetic resonance imaging (MRI)/magnetic resonance cholangiopancreatography (MRCP), and positron emission tomography (PET)/CT. Computational techniques might help to predict actionable genetic alterations, assist with biomarker selection, and integrate imaging analysis with histological and genomic data within pathology and molecular oncology. AI may also contribute to prognostic modeling and personalized treatment selection, including surgery, transplantation protocols, systemic therapy, targeted therapy, immunotherapy, locoregional therapy, biliary drainage, and surveillance planning. However, most of the available studies were retrospective, single-center, and insufficiently validated. Key obstacles consist of limited datasets, variability in disease characteristics, inconsistent reference standards, challenges in model interpretability, difficulties in workflow integration, regulatory concerns, and unpredictable clinical outcomes. This review outlines both current and developing uses of AI in cholangiocarcinoma, ranging from ambiguous biliary strictures to tailored treatment options, compares progress in this field with the more mature experience of radiology, pathology, and gastrointestinal endoscopy, and summarizes emerging guidance for the safe development, reporting, and governance of clinical AI. It emphasizes the necessity for prospective, externally validated multimodal AI systems that enhance multidisciplinary clinical decision-making.
Keywords: Artificial intelligence, Cholangiocarcinoma, Indeterminate biliary stricture, Radiomics
SUPPORTING INFORMATION
Acknowledgments
AI-generated content acknowledgement:
During the preparation of this manuscript, artificial intelligence-assisted language tools were used only to support language editing, grammar refinement, and improvement of readability. No AI tool was used to generate original scientific data, perform data analysis, create references, or replace the authors’ interpretation and critical judgment. The authors reviewed, edited, and approved all content and take full responsibility for the accuracy and integrity of the final manuscript.
Ahmed Salman - Substantial contributions to conception and design, Revising it critically for important intellectual content, Final approval of the version to be published
Ahmed Elewa - Substantial contributions to conception and design, Interpretation of data, Drafting the article, Final approval of the version to be published
Ahmed Marwan - Substantial contributions to conception and design, Interpretation of data, Drafting the article, Final approval of the version to be published
Guarantor of SubmissionThe corresponding author is the guarantor of submission.
Source of SupportNone
Data AvailabilityAll relevant data are within the paper and its Supporting Information files.
Conflict of InterestAuthors declare no conflict of interest.
Copyright© 2026 Ahmed Salman et al. This article is distributed under the terms of Creative Commons Attribution License which permits unrestricted use, distribution and reproduction in any medium provided the original author(s) and original publisher are properly credited. Please see the copyright policy on the journal website for more information.
