SynthesisInternational journal of colorectal disease2025
Effectiveness of artificial intelligence-assisted colonoscopy in detecting and diagnosing colorectal tumors: a systematic review and network meta-analysis.
Synthesis in International journal of colorectal disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Korean Colorectal Cancer Screening Guidelines for Asymptomatic, Average-Risk Adults: The 2025 Revision.Cancer research and treatment · 2026Pooled it
- Concordance Between a Generative Artificial Intelligence Model and a Hepatobiliary Multidisciplinary Team in Hepatocellular Carcinoma Management: A Retrospective Study.Annals of surgical oncology · 2026Article
- Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology.Cancers · 2026Review
- Usefulness of Artificial Intelligence for Transnasal Esophagogastroduodenoscopy in Clinical Practice.Cureus · 2026Article
- Human-AI Systems in Medicine: Outskilling Versus Newskilling.Annals of biomedical engineering · 2026Article
- Advancements in interventional gastroenterology GI tract cancers: A narrative review.Medicine · 2026Review
- Hemorrhoids and Rectal Cancer: A Systematic Review of Clinical Overlap, Diagnostic Misclassification, and Early Detection Strategies for Primary Care.Journal of multidisciplinary healthcare · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundThe emergence of artificial intelligence (AI) has greatly promoted the development of the field of medical image analysis, but the potential benefits of AI-assisted colonoscopy and diagnosis (CADe/CADx) for the detection rate of colorectal adenomas and the histological diagnosis of polyps are still controversial and unknown.
methodsWe conducted a search on PubMed, Web of Science, Embase, and Cochrane, and the last search time was August 2024. We collected adenoma detection rate (ADR), polyp detection rate (PDR), and sessile serrated lesion detection rate (SSL). Paired analysis and network meta-analysis (NMA) were performed using R Studio. StataSE15.0 software was used for statistical analysis to calculate the sensitivity and specificity of CADx and conventional colonoscopy.
resultsWe included a total of 64 studies, including 52 RCT studies and 12 clinical studies, with a total of 50,834 patients undergoing colonoscopy. The results showed that different adjuvant interventions had significant differences in the detection rate of adenoma compared with routine colonoscopy ADR [RR = 1.20, 95% CI (1.14, 1.26), P < 0.001], and the results were statistically significant. Among different CADe models and advanced optical imaging techniques, ENDOANGEL model-assisted colonoscopy is the most effective method for detecting colorectal adenomas and polyps (97.8%), and Endocuff-AI model-assisted colonoscopy is the most effective method for detecting sessile serrated lesions (94.4%). In the performance study of endoscopists with or without CADX-assisted diagnosis, the optical diagnostic sensitivity of colorectal adenomas was (88% VS 86%), specificity (78% VS 77%), and AUC area (91% VS 89%), and the study results showed no significant differences.
conclusionENDOANGEL model-assisted colonoscopy shows the best efficacy on both ADR and PDR, Endocuff-AI model-assisted colonoscopy shows the best performance on SSL, and compared with optical evaluation without CADx, real-time polyp assessment using CADx did not significantly increase the diagnostic sensitivity of neoplastic polyps during colonoscopy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.