Evidence map›Paper›PMID 41417282›Full record

SynthesisJournal of gastrointestinal cancer2025

Improving Colorectal Cancer Detection with AI-Assisted Colonoscopy: A Systematic Review and Meta-Analysis of 38 RCTs with GRADE Assessment.

Israa Ahmed Qutob, Asmaa Soliman, Rayhana Trabelsi, Mohamed Karam Allah Elkholy, Ali Salah Elgahamy, Maha Abuismail, Mohamed Nabil Hamouda, Islam Elhaddad, Ahmed Abdelaziz Eldaly, Abdullrahman Elgarawany and 3 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Journal of gastrointestinal cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Israa Ahmed QutobFaculty of Medicine, Alexandria University, Alexandria, Egypt. esraa.ahmed2101@alexmed.edu.eg.ORCID http://orcid.org/0009-0005-2173-9984
Asmaa SolimanFaculty of Medicine, Zagazig University, Sharqia, Egypt.ORCID http://orcid.org/0009-0006-5761-0186
Rayhana TrabelsiMedical Control Service, CNAS, Touggourt, Algeria.ORCID http://orcid.org/0009-0002-6474-7691
Mohamed Karam Allah ElkholyFaculty of Pharmacy, Zagazig University, Sharqia, Egypt.ORCID http://orcid.org/0009-0005-7937-9889
Ali Salah ElgahamyDepartment of Biochemistry, Faculty of Science, Al Azhar University, Cairo, Egypt.ORCID http://orcid.org/0000-0001-9685-8869
Maha AbuismailFaculty of Pharmacy, JUST - Jordan University of Science and Technology, Irbid, Jordan.ORCID http://orcid.org/0009-0001-5274-4963
Mohamed Nabil HamoudaFaculty of Medicine, Misr University for Science and Technology, Giza, Egypt.ORCID http://orcid.org/0009-0000-2750-5859
Islam ElhaddadFaculty of Medicine, Al-Azher University , New Damietta, Egypt.ORCID http://orcid.org/0009-0007-4969-2764
Ahmed Abdelaziz EldalyFaculty of Medicine, Mansoura University, Dakahlia, Egypt.ORCID http://orcid.org/0009-0003-2863-4136
Abdullrahman ElgarawanyFaculty of Medicine, Galala University, Suez, Egypt.ORCID http://orcid.org/0009-0007-0717-9225
Sohaila O BoshirtelaFaculty of Medicine, Omar Al-Mukhtar University, Bayda, Libya.ORCID http://orcid.org/0009-0003-6199-0044
Sumaya MohammedFaculty of Medicine, Al Anbar University, Ramadi, Iraq.ORCID http://orcid.org/0009-0004-0150-3192
Sally SeliemFaculty of Medicine, Mansoura University, Dakahlia, Egypt.ORCID http://orcid.org/0009-0000-6059-307X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Early detection of precancerous lesions such as adenomas and polyps is vital for prevention, yet standard colonoscopy may miss up to 26% of adenomas. Artificial intelligence (AI)-assisted colonoscopy has emerged as a promising tool to enhance lesion detection. This meta-analysis provides an updated synthesis of randomized controlled trials (RCTs) evaluating the diagnostic performance of AI-assisted versus standard colonoscopy.

methodsFollowing PRISMA guidelines, a comprehensive search of PubMed, Cochrane, Web of Science, and Scopus identified 38 RCTs including 29,745 participants. Pooled risk ratios (RRs) or mean differences (MDs) with 95% confidence intervals (CIs) were calculated using random-effects models. Subgroup analyses by geographical region and AI manufacturer were performed to explore heterogeneity. Certainty of evidence was rated using the GRADE approach, and publication bias was assessed through funnel plots and Egger's test.

resultsAI-assisted colonoscopy significantly improved adenoma detection rate (ADR: RR = 1.19, 95% CI 1.14-1.25) and polyp detection rate (PDR: RR = 1.19, 95% CI 1.13-1.25), while reducing adenoma miss rate (AMR: RR = 0.52, 95% CI 0.38-0.72) and polyp miss rate (PMR: RR = 0.53, 95% CI 0.33-0.88). The mean number of adenomas per colonoscopy increased (MD = 0.20, 95% CI 0.14-0.25). AI modestly prolonged withdrawal time (MD = 0.46 min, 95% CI 0.26-0.66). No significant difference was observed for carcinoma detection (RR = 1.12) or advanced adenoma miss rate (RR = 0.67). Sessile serrated lesion detection showed a nonsignificant trend toward improvement (RR = 1.13), becoming significant after outlier exclusion (RR = 1.23). Subgroup analyses revealed higher ADR improvement in East Asian studies and with specific AI systems (e.g., Wision AI). Egger's test suggested minor small-study effects for ADR and withdrawal time.

conclusionsAI-assisted colonoscopy substantially enhances adenoma and polyp detection while reducing miss rates. However, its benefit for carcinoma and sessile serrated lesion detection remains uncertain. Further standardized RCTs with long-term follow-up are warranted to confirm its role in colorectal cancer screening.

Indexed as

AdenomaArtificial IntelligenceColonoscopyColorectal NeoplasmsEarly Detection of CancerColonic PolypsHumansRandomized Controlled Trials as TopicAdenoma detection rate (ADR)Artificial intelligence (AI)ColonoscopyColorectal cancer (CRC) preventionPolyp detection

Identifiers

PMID41417282

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.