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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 14, 2025; 31(38): 110999
Published online Oct 14, 2025. doi: 10.3748/wjg.v31.i38.110999
Published online Oct 14, 2025. doi: 10.3748/wjg.v31.i38.110999
Can artificial intelligence improve the diagnosis and management of patients with eosinophilic esophagitis?
Iyad A Issa, Department of Gastroenterology and Hepatology, Harley Street Medical Center, Abu Dhabi 41475, United Arab Emirates
Osama Youssef, Department of Gastroenterology and Hepatology, Cleveland Clinic Abu Dhabi, Abu Dhabi 112412, United Arab Emirates
Taly Issa, Medical School, University of Nicosia, Nicosia 24005, Cyprus
Author contributions: Issa IA, Youssef O and Issa T contributed to this paper; Issa IA designed the overall concept and outline of the manuscript; Issa T and Youssef O contributed to the discussion and design of the manuscript; Issa IA, Youssef O and Issa T contributed to the writing, and editing the manuscript, illustrations, and review of literature; All authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Iyad A Issa, MD, Doctor, Department of Gastroenterology and Hepatology, Harley Street Medical Center, Marina Village, Villa No. A21, Abu Dhabi 41475, United Arab Emirates. iyadissa71@gmail.com
Received: June 20, 2025
Revised: July 14, 2025
Accepted: September 8, 2025
Published online: October 14, 2025
Processing time: 116 Days and 12.8 Hours
Revised: July 14, 2025
Accepted: September 8, 2025
Published online: October 14, 2025
Processing time: 116 Days and 12.8 Hours
Core Tip
Core Tip: The integration of artificial intelligence (AI) and deep learning models into the diagnosis and management of eosinophilic esophagitis shows great promise in enhancing diagnostic accuracy, reducing human variability in interpretation, and facilitating timely interventions. By addressing the limitations of traditional diagnostic methods, AI technologies can streamline the clinical workflow, improve patient outcomes, and potentially transform the approach to managing this complex, chronic condition. However, ongoing research is necessary to validate these tools across diverse clinical settings and ensure their ethical application.