Systematic Reviews
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Jun 8, 2025; 6(1): 106149
Published online Jun 8, 2025. doi: 10.35712/aig.v6.i1.106149
Current status of artificial intelligence colonoscopy on improving adenoma detection rate based on systematic review of multiple metanalysis
Maryam A Aleissa, Micheal Luca, Jai P Singh, Gautham Chitragari, Ernesto R Drelichman, Vijay K Mittal, Jasneet S Bhullar
Maryam A Aleissa, Department of Surgery, Henry Ford Providence Hospital, Michigan State University College of Human Medicine, Southfeild, MI 48075, United States
Maryam A Aleissa, Collage of Medicine, Princess Nourah bint Abdulrhman University, Riyadh 84428, Saudi Arabia
Micheal Luca, Jai P Singh, Gautham Chitragari, Ernesto R Drelichman, Vijay K Mittal, Jasneet S Bhullar, Department of Surgery, Henry Ford Providence Hospital, Michigan State University College of Human Medicine, Southfield, MI 48075, United States
Author contributions: Aleissa MA, Luca M perform the research and collect the data; Sing JP, Chitragari G, Drelichman ER, Mittal VK, Bhullar JS design and edits the research
Conflict-of-interest statement: All authors declare no conflicts of interest to disclose.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Jasneet S Bhullar, FASCRS, MD, Department of Surgery, Henry Ford Providence Hospital, Michigan State University College of Human Medicine, 16001 W Nine Mile Rd, Southfield, MI 48075, United States. drjsbhullar@gmail.com
Received: February 18, 2025
Revised: March 23, 2025
Accepted: May 8, 2025
Published online: June 8, 2025
Processing time: 109 Days and 5.7 Hours
Abstract
BACKGROUND

Colorectal cancer (CRC) can be prevented by screening and early detection. Colonoscopy is used for screening, and adenoma detection rate (ADR) is used as a key quality indicator of sufficient colonoscopy. However, ADR can vary significantly among endoscopists, leading to missed polyps or cancer. Artificial intelligence (AI) has shown promise in improving ADR by assisting in real-time polyp identification or diagnosis. While multiple randomized controlled trials (RCTs) and metanalyses highlight the benefits of AI in increasing detection rates and reducing missed polyps, concerns remain about its real-world applicability, impact on procedure time, and cost-effectiveness.

AIM

To explore the current status of AI assistance colonoscopy in adenoma detection and improving quality of colonoscopy.

METHODS

This systematic review followed PRISMA guidelines, both PubMed and Web of Science databases were used for articles search. Metanalyses and systematic reviews that assessed AI's role during colonoscopy. English article only published between January 2000 and January 2025 were included. Articles related to non-adenoma indications were excluded. Data extraction was independently performed by two researchers for accuracy and consistency.

RESULTS

22 articles met the inclusion criteria, with significant heterogeneity (I2 = 28%-91%) observed in multiple studies. The number of studies per metanalysis ranged from 5 to 33, with higher heterogeneity in analyses involving more than 18 RCTs. AI demonstrated improvement in ADR, with an approximate 20% increase across multiple studies. However, its effectiveness in detecting flat or serrated adenomas remains unproven. Endoscopists with low ADR benefit more from AI-colonoscopies, while expert endoscopists outperformed AI in ADR, adenoma miss rate, and the identification of advanced lesions. No significant change in withdrawal time was observed when comparing AI-assisted colonoscopy to conventional endoscopy.

CONCLUSION

While AI-assisted colonoscopy has been shown to improve procedural quality, particularly for junior endoscopists and those with lower ADR, its performance decreases when compared to expert endoscopists in real-time clinical practice. This is especially evident in non-randomized studies, where AI demonstrates limited real-world benefits despite its benefit in controlled settings. Furthermore, no meta-analyses have specifically examined AI's impact on the learning experience of fellows and residents. Some experts caution that reliance on AI may prevent trainees from developing essential observational skills, potentially leading to less thorough examinations. Further research is needed to determine the actual benefits of AI-colonoscopy, particularly its role in cancer prevention. As technology advances, improved outcomes are expected, especially in detecting small, flat, and lesions at difficult anatomical locations.

Keywords: Artificial intelligence; Artificial intelligence assistance colonoscopy; Adenoma detection rate; Colon cancer prevention; Colonoscopy

Core Tip: Artificial intelligence (AI) has shown promising potential in improving the adenoma detection rate (ADR) during colonoscopy, particularly for junior endoscopists and those with a lower baseline ADR. However, expert endoscopists continue to outperform AI in real-world settings, especially in detecting flat and serrated lesions. While the implementation of AI-assisted colonoscopy does not significantly impact withdrawal time, its effectiveness in routine clinical practice remains uncertain. Future research should focus on the role of AI-assisted colonoscopy in colorectal cancer prevention, its impact on resident and fellow training, and its ability to enhance the detection of challenging lesions.