Effectiveness of artificial intelligence-assisted colonoscopy for colorectal lesion detection: a systematic review and meta-analysis of randomized controlled trials
- 1Department of Basic Medicine, Sichuan Vocational College of Health and Rehabilitation
- 2Department of General Surgery, Zigong Fourth People’s Hospital
Abstract
Background Colonoscopy is the gold standard for colorectal cancer detection and prevention, but its performance depends on operator skill and lesion features. Artificial intelligence (AI)-assisted colonoscopy has been introduced to improve adenoma detection. Methods A comprehensive search was conducted in PubMed, Embase, the Cochrane Library, Web of Science, ClinicalTrials.gov, and major Chinese databases to identify randomized controlled trials (RCTs) comparing AI-assisted and conventional colonoscopy. Meta-analyses were performed using Review Manager 5.4 and Stata 14.0. The quality of evidence was graded using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system, while trial sequential analysis (TSA) was applied to assess the robustness and information sufficiency of the results. Results A total of 46 RCTs encompassing 37,012 participants were included. Compared with conventional colonoscopy, AI-assisted colonoscopy was associated with a higher ADR (RR = 1.22, 95% CI = 1.17–1.27, P < 0.00001), polyp detection rate (PDR: RR = 1.22, 95% CI = 1.16–1.29, P < 0.00001), polyp detection per colonoscopy (PPC: MD = 0.28, 95% CI = 0.21–0.36), and adenoma detection per colonoscopy (APC: MD = 0.22, 95% CI = 0.15–0.29). The detection rate of sessile serrated lesions (SSL) was also higher in the AI-assisted group (RR = 1.20, 95% CI: 1.05–1.36, P = 0.007), whereas no significant difference was observed in advanced adenoma detection rate (AADR). Conclusions AI-assisted colonoscopy was associated with improvements in several detection-related quality indicators, including ADR, PDR, PPC, APC, and SSL detection rate. However, given the very low GRADE certainty for ADR and PDR and the lack of long-term outcome data, these findings should be interpreted cautiously. Clinical trial number Not applicable.
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BibTeX
@article{liu2026effectiveness,
title = {{Effectiveness of artificial intelligence-assisted colonoscopy for colorectal lesion detection: a systematic review and meta-analysis of randomized controlled trials}},
author = {Xia Liu and Jian Qin and Yuxia Zhao and Chen Zhang and Dengchao Wang and Tao Jiang},
journal = {BMC Medical Imaging},
year = {2026},
month = jul,
doi = {10.1186/s12880-026-02586-8},
url = {https://doi.org/10.1186/s12880-026-02586-8},
note = {CC BY},
}
RIS
TY - JOUR
TI - Effectiveness of artificial intelligence-assisted colonoscopy for colorectal lesion detection: a systematic review and meta-analysis of randomized controlled trials
AU - Xia Liu
AU - Jian Qin
AU - Yuxia Zhao
AU - Chen Zhang
AU - Dengchao Wang
AU - Tao Jiang
PY - 2026
DA - 2026/07/01
JO - BMC Medical Imaging
DO - 10.1186/s12880-026-02586-8
UR - https://doi.org/10.1186/s12880-026-02586-8
AB - Background Colonoscopy is the gold standard for colorectal cancer detection and prevention, but its performance depends on operator skill and lesion features. Artificial intelligence (AI)-assisted colonoscopy has been introduced to improve adenoma detection. Methods A comprehensive search was conducted in PubMed, Embase, the Cochrane Library, Web of Science, ClinicalTrials.gov, and major Chinese databases to identify randomized controlled trials (RCTs) comparing AI-assisted and conventional colonoscopy. Meta-analyses were performed using Review Manager 5.4 and Stata 14.0. The quality of evidence was graded using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system, while trial sequential analysis (TSA) was applied to assess the robustness and information sufficiency of the results. Results A total of 46 RCTs encompassing 37,012 participants were included. Compared with conventional colonoscopy, AI-assisted colonoscopy was associated with a higher ADR (RR = 1.22, 95% CI = 1.17–1.27, P < 0.00001), polyp detection rate (PDR: RR = 1.22, 95% CI = 1.16–1.29, P < 0.00001), polyp detection per colonoscopy (PPC: MD = 0.28, 95% CI = 0.21–0.36), and adenoma detection per colonoscopy (APC: MD = 0.22, 95% CI = 0.15–0.29). The detection rate of sessile serrated lesions (SSL) was also higher in the AI-assisted group (RR = 1.20, 95% CI: 1.05–1.36, P = 0.007), whereas no significant difference was observed in advanced adenoma detection rate (AADR). Conclusions AI-assisted colonoscopy was associated with improvements in several detection-related quality indicators, including ADR, PDR, PPC, APC, and SSL detection rate. However, given the very low GRADE certainty for ADR and PDR and the lack of long-term outcome data, these findings should be interpreted cautiously. Clinical trial number Not applicable.
SN - 1471-2342
ER -