Rahmoune H, Boutrid N, Benchoufi I. Precision medicine in celiac disease: A step ahead. Artif Intell Gastroenterol 2025; 6(1): 105682 [DOI: 10.35712/aig.v6.i1.105682]
Corresponding Author of This Article
Hakim Rahmoune, MD, Assistant Professor, LIRSSEI Research Laboratory, Faculty of Medicine, University of Setif-1, Setif 19000, Algeria. rahmounehakim@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
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. Jun 8, 2025; 6(1): 105682 Published online Jun 8, 2025. doi: 10.35712/aig.v6.i1.105682
Precision medicine in celiac disease: A step ahead
Hakim Rahmoune, Nada Boutrid, Isra Benchoufi
Hakim Rahmoune, Nada Boutrid, LIRSSEI Research Laboratory, Faculty of Medicine, University of Setif-1, Setif 19000, Algeria
Hakim Rahmoune, Department of Pediatrics, Setif University Hospital, Setif 19000, Algeria
Nada Boutrid, Department of Pediatrics, El Eulma Mother and Child Hospital, El Eulma 19000, Setif, Algeria
Isra Benchoufi, Department of Artificial Intelligence, National School of Artificial Intelligence, Algiers 16000, Alger, Algeria
Author contributions: Rahmoune H and Boutrid N contributed to conceptualization; Rahmoune H contributed to data curation; Boutrid B contributed to supervision and validation; Boutrid N Benchoufi I contributed to writing-original draft, review and editing.
Conflict-of-interest statement: The authors have nothing to disclose.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hakim Rahmoune, MD, Assistant Professor, LIRSSEI Research Laboratory, Faculty of Medicine, University of Setif-1, Setif 19000, Algeria. rahmounehakim@gmail.com
Received: February 5, 2025 Revised: April 5, 2025 Accepted: May 24, 2025 Published online: June 8, 2025 Processing time: 123 Days and 17.3 Hours
Abstract
Celiac disease (CD) is a common autoimmune disorder where gluten ingestion triggers an immune response, damaging the small intestine in genetically predisposed individuals. Affecting around 1% of the global population, CD presents with diverse symptoms, including gastrointestinal issues like diarrhea and extraintestinal conditions such as anemia and osteoporosis, often complicating diagnosis. Advances in serology, histology, and genetic testing, such as HLA-DQ2/DQ8 analysis, have improved diagnostic accuracy. Precision medicine is transforming CD management by integrating genetic, clinical, and lifestyle data to enable risk prediction, personalized therapies, and improved outcomes. Tools like machine learning enhance early diagnosis, dietary management, and drug discovery, while electronic medical records support comprehensive patient profiling and disease monitoring. These technologies facilitate personalized healthcare delivery tailored to individual patient profiles.
Core Tip: Advances in precision medicine, powered by artificial intelligence (AI), machine learning (ML) and deep learning (DL), are transforming the diagnosis and management of celiac disease (CD). By integrating histopathologic features, genetic data, and electronic medical records (EMRs), these technologies enable accurate disease classification, risk prediction, and personalized treatment strategies. ML and DL models have demonstrated high accuracy in predicting CD severity, while EMR-based approaches facilitate tailored dietary and therapeutic interventions. These innovations underscore the potential of AI-driven methodologies to enhance patient outcomes and revolutionize the clinical approach to CD.
