Dimopoulou K, Spinou M, Ioannou A, Nakou E, Zormpas P, Tribonias G. Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes. World J Gastroenterol 2025; 31(40): 111499 [DOI: 10.3748/wjg.v31.i40.111499]
Corresponding Author of This Article
Konstantina Dimopoulou, MD, PhD, Consultant, Department of Gastroenterology, Red Cross Hospital, 1, Athanasaki str, Athens 11526, Greece. conu_med@hotmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Review
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/
Oct 28, 2025 (publication date) through Oct 30, 2025
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Journal Information of This Article
Publication Name
World Journal of Gastroenterology
ISSN
1007-9327
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Dimopoulou K, Spinou M, Ioannou A, Nakou E, Zormpas P, Tribonias G. Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes. World J Gastroenterol 2025; 31(40): 111499 [DOI: 10.3748/wjg.v31.i40.111499]
Konstantina Dimopoulou, Marianna Spinou, Eleni Nakou, Petros Zormpas, George Tribonias, Department of Gastroenterology, Red Cross Hospital, Athens 11526, Greece
Alexandros Ioannou, Department of Gastroenterology, Alexandra, General Hospital of Athens, Athens 11528, Greece
Co-first authors: Konstantina Dimopoulou and Marianna Spinou.
Author contributions: Dimopoulou K, Spinou M, Tribonias G contributed to conceptualization; Dimopoulou K, Spinou M, Ioannou A, Nakou E, Zormpas P, Tribonias G contributed to methodology, data acquisition, investigation, writing draft preparation, writing review and editing; Dimopoulou K, Spinou M, Tribonias G contributed to supervision and project administration; All authors have read and approved the final version of the manuscript; Dimopoulou K and Spinou M contributed equally as first authors. Both shared responsibility for the conception and design of the study, acquisition and analysis of data, interpretation of results, and drafting and critical revision of the manuscript. Their equal contributions meet the criteria typically attributed to the first author, reflecting substantial intellectual involvement in all key stages of the research and publication process.
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: Konstantina Dimopoulou, MD, PhD, Consultant, Department of Gastroenterology, Red Cross Hospital, 1, Athanasaki str, Athens 11526, Greece. conu_med@hotmail.com
Received: July 1, 2025 Revised: August 9, 2025 Accepted: September 18, 2025 Published online: October 28, 2025 Processing time: 118 Days and 12 Hours
Core Tip
Core Tip: Artificial intelligence (AI) has emerged as a breakthrough innovation in modern colonoscopy, offering significant improvements in key quality indicators, including adenoma detection rate, polyp detection rate, adenoma miss rate, bowel preparation assessment, withdrawal time, and cecal intubation recognition. By optimizing lesion characterization, supporting optical diagnosis and assisting in invasion depth prediction, AI may lead to early diagnosis and prevention of colorectal cancer. This review summarizes the current evidence on the application of AI in key colonoscopy quality indicators, highlighting its role in standardizing practice, improving patient outcomes, and advancing personalized care while also addressing the challenges of training, cost-effectiveness, and ethical considerations.
