Published online Jun 8, 2025. doi: 10.35712/aig.v6.i1.106149
Revised: March 23, 2025
Accepted: May 8, 2025
Published online: June 8, 2025
Processing time: 109 Days and 5.7 Hours
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 sig
To explore the current status of AI assistance colonoscopy in adenoma detection and improving quality of colonoscopy.
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 per
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.
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.
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.