Salman A, Elewa A, Marwan A. Artificial intelligence in autoimmune pancreatitis: Differentiating immunoglobulin G4-related pancreatitis from pancreatic ductal adenocarcinoma. Artif Intell Gastroenterol 2026; 7(3): 123890 [DOI: 10.35712/aig.123890]
Corresponding Author of This Article
Ahmed Salman, Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Cairo 11562, Egypt. awea844@gmail.com
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. Nov 8, 2026; 7(3): 123890 Published online Nov 8, 2026. doi: 10.35712/aig.123890
Artificial intelligence in autoimmune pancreatitis: Differentiating immunoglobulin G4-related pancreatitis from pancreatic ductal adenocarcinoma
Ahmed Salman, Ahmed Elewa, Ahmad Marwan
Ahmed Salman, Internal Medicine, Kasr Alainy School of Medicine, Cairo 11562, Egypt
Ahmed Elewa, General Surgery, National Hepatology and Tropical Medicine Liver Institute, Cairo 16A, Egypt
Ahmad Marwan, Internal Medicine, Faculty of Medicine, Mansoura University, Mansoura 3153, Egypt
Author contributions: Salman A conceived and designed the review, performed the literature search, and drafted the manuscript; Elewa A critically revised the manuscript for important intellectual content and approved the final version; Marwan A conceived the idea of the work and contributed to drafting and critical revision; and all authors read and approved the final version of the manuscript.
AI contribution statement: The authors used artificial-intelligence-based tools (ChatGPT 5.6) solely for language editing and grammatical corrections. No artificial-intelligence tool was used to generate, analyze, or interpret data, to draft scientific content, to formulate conclusions, or to select references. All content was reviewed and verified by the authors, who take full responsibility for the accuracy, originality, and integrity of the manuscript.
Conflict-of-interest statement: There are no conflicts of interest to report.
Corresponding author: Ahmed Salman, Internal Medicine, Kasr Alainy School of Medicine, 1 Al-Saray Street, Cairo 11562, Egypt. awea844@gmail.com
Received: June 2, 2026 Revised: July 30, 2026 Accepted: August 10, 2026 Published online: November 8, 2026 Processing time: 155 Days and 23.8 Hours
Abstract
Autoimmune pancreatitis (AIP), specifically the immunoglobulin G4-related type-1 variant, serves as a significant benign mimic of pancreatic ductal adenocarcinoma (PDAC). Focal or mass-forming AIP can manifest as obstructive jaundice, a pancreatic mass, ductal narrowing, weight loss, and elevated tumor markers that may resemble those of PDAC. Misdiagnosis can result in either unnecessary pancreatic surgery or delayed treatment for cancer. Artificial intelligence (AI), including radiomics, machine learning, deep learning, and multimodal decision-support systems, offers a possible solution for this diagnostic dilemma. AI-based imaging is able to extract quantitative characteristics from a variety of modalities, such as computed tomography, magnetic resonance imaging, diffusion-weighted imaging, positron emission tomography, and endoscopic ultrasonography that may not be apparent by visual inspection. Digital pathology and models combining radiological, serological, histological and clinical variables are also on the rise. Current data is limited by small retrospective single-center cohorts, uneven imaging techniques, dependency on segmentation, and the absence of adequate external validation. AI should be seen as a supplement, not a substitute, to the professional multidisciplinary assessment, boosting the risk classification, directing the tissue collection, and steroid trials, and avoiding unnecessary procedures without delaying PDAC diagnosis. Future research should target prospective multicenter validation, uniform datasets, improved explainability and therapeutically significant objectives.
Core Tip: Autoimmune pancreatitis, especially localized immunoglobulin G4-related condition, is very similar to pancreatic ductal adenocarcinoma and constitutes a great diagnostic dilemma. This review summarizes the emerging applications of artificial intelligence (AI) such as radiomics, deep learning, endoscopic ultrasound image analysis, digital pathology, and multimodal models to aid in this differentiation and provides a table of reported diagnostic performance in published studies. AI can help improve risk classification and decrease unnecessary surgical procedures. However, existing evidence is insufficient, and its application should complement expert multidisciplinary evaluations, necessitating thorough validation to prevent delays in cancer diagnosis.
