Issa IA, Youssef O, Issa T. Can artificial intelligence improve the diagnosis and management of patients with eosinophilic esophagitis? World J Gastroenterol 2025; 31(38): 110999 [DOI: 10.3748/wjg.v31.i38.110999]
Corresponding Author of This Article
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
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/
World J Gastroenterol. Oct 14, 2025; 31(38): 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, Osama Youssef, Taly Issa
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 12.6 Hours
Abstract
Eosinophilic esophagitis (EoE) is a chronic, immune-mediated condition leading to esophageal inflammation and a range of symptomatic complications if inadequately managed. Recent epidemiological trends indicate a significant increase in EoE prevalence, complicating patient care amid diagnostic challenges associated with conventional methods such as endoscopy and histopathological analysis. This review explores the promise of artificial intelligence (AI) and deep learning models in enhancing the diagnosis and management of EoE, addressing the limitations of traditional approaches including inter-observer variability, invasiveness, and delays in diagnosis. By synthesizing findings from peer-reviewed studies, we demonstrate that AI algorithms exhibit high diagnostic accuracy in recognizing subtle endoscopic features and quantifying eosinophilic tissue infiltration. Moreover, these technologies can streamline workflows, reduce dependency on manual assessments, and enhance personalized care strategies. Despite the potential benefits, challenges regarding the integration of AI into clinical practice remain, including issues of algorithmic bias, data privacy, and the need for robust validation across diverse healthcare settings. Future research should focus on multicenter studies to confirm AI’s effectiveness, explore non-invasive diagnostic alternatives, and promote the ethical application of AI to optimize patient outcomes in EoE. This review highlights AI’s transformative capacity to reshape the diagnostic landscape of EoE, underscoring the requirement for ongoing evaluation and collaboration among clinicians, researchers, and technology developers to realize its full potential within the healthcare framework.
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.
