El Asmar N, Baydoun M, Mrad J, Barada K. Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions. World J Gastroenterol 2025; 31(44): 111160 [DOI: 10.3748/wjg.v31.i44.111160]
Corresponding Author of This Article
Kassem Barada, MD, Professor, Division of Gastroenterology and Hepatology, Department of Internal Medicine, American University of Beirut Medical Center, Riad El Solh PO Box 11-0236, Beirut 1107 2020, Lebanon. kb02@aub.edu.lb
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Gastroenterology & Hepatology
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Review
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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/
Nov 28, 2025 (publication date) through Dec 1, 2025
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World Journal of Gastroenterology
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1007-9327
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El Asmar N, Baydoun M, Mrad J, Barada K. Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions. World J Gastroenterol 2025; 31(44): 111160 [DOI: 10.3748/wjg.v31.i44.111160]
World J Gastroenterol. Nov 28, 2025; 31(44): 111160 Published online Nov 28, 2025. doi: 10.3748/wjg.v31.i44.111160
Role of artificial intelligence in the detection and characterization of gastrointestinal premalignant and early malignant lesions
Noelle El Asmar, Mariam Baydoun, Jamil Mrad, Kassem Barada
Noelle El Asmar, Mariam Baydoun, Jamil Mrad, Kassem Barada, Division of Gastroenterology and Hepatology, Department of Internal Medicine, American University of Beirut Medical Center, Beirut 1107 2020, Lebanon
Author contributions: El Asmar N, Baydoun M, Mrad J compiled and analysed the data and drafted the manuscript; Barada K conceptualized, supervised and critically reviewed the manuscript for important intellectual content.
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: Kassem Barada, MD, Professor, Division of Gastroenterology and Hepatology, Department of Internal Medicine, American University of Beirut Medical Center, Riad El Solh PO Box 11-0236, Beirut 1107 2020, Lebanon. kb02@aub.edu.lb
Received: June 25, 2025 Revised: July 20, 2025 Accepted: October 23, 2025 Published online: November 28, 2025 Processing time: 156 Days and 21.8 Hours
Abstract
Artificial intelligence (AI) is revolutionizing the field of gastrointestinal (GI) endoscopy, a technology that relies heavily on images and optical data. Precancerous lesions and early cancers of the GI tract can be subtle and easily missed even on high-definition endoscopy and chromoendoscopy. The advancements in machine learning and deep learning led to the development of computer-aided models of high performance in image analysis. The convolutional neural networks of these models are trained to analyze large datasets of endoscopic images through the supervised learning approach. Their utilization enhances lesion detection and visibility. This aids in real-time classification and risk stratification of GI luminal lesions, thus assisting endoscopists in making more accurate and timely decisions. AI has shown promising results in the detection and characterization of premalignant and early malignant lesions of the GI tract, such as Barrett’s esophagus, gastric atrophy, intestinal metaplasia, small bowel and colonic polyps, as well as early esophageal, gastric and colon cancers. This positive impact of AI is more established in the esophagus and stomach than in the colon. However, the impact of AI on patients’ outcomes such as mortality and interval cancer incidence remains to be seen. This review highlights the breakthroughs and clinical applications of AI in the detection and characterization of premalignant lesions and early cancers of the GI tract.
Core Tip: Artificial intelligence (AI) is revolutionizing healthcare. There is evidence supporting the role of AI in the detection/characterization of premalignant lesions and early cancers of the gastrointestinal (GI) tract, especially in the esophagus, stomach and colon. The utilization of AI may eventually decrease the incidence of interval cancers which are mainly attributed to missed lesions during endoscopy. It benefits mostly novice endoscopists and trainees. AI may be superior to experienced endoscopists in the detection of diminutive lesions. The effect of AI on clinically meaningful outcomes such as incidence of interval GI cancers, morbidity, mortality and cost effectiveness requires further research.
