ReviewDigestion2025
Artificial Intelligence in Colonoscopy: Where Are We Now in 2024?
Review in Digestion, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- PolypSAM-Open: Mitigating Automation Bias in AI-Assisted Colonoscopy via Open-Set Surgical Artifact Rejection.Diagnostics (Basel, Switzerland) · 2026Article
- Is the use of artificial intelligence the main stage for detecting polyps during colonoscopy?World journal of gastroenterology · 2025Article
- Use of Modified YOLOv5 Algorithm in the Differential Diagnosis of Colonic Crohn's Disease and Ulcerative Colitis on CTE Images.Gastroenterology research and practice · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionColonoscopy has a crucial role in reducing colorectal cancer incidence and mortality. Different artificial intelligence (AI) systems were developed to further improve its quality assurance (computer-aided quality improvement [CAQ]), lesion detection (computer-aided detection [CADe]), and lesion characterization (computer-aided characterization [CADx]). There were studies investigating the roles of these AI systems in different domains of standard colonoscopies.
methodsIn this state-of-the-art narrative review, we summarize the current evidence, discuss existing limitations, as well as explore the future directions of AI in colonoscopy.
resultsCAQ enhances colonoscopy quality through real-time feedback and quality monitoring systems, but the studies have inconsistent results due to small training datasets and varied methodologies. CADe increases adenoma detection rate and reduces adenoma missed rates, but there are concerns about false positives, unnecessary polypectomies, potential deskilling of endoscopists, and cost-effectiveness. CADx systems have mixed results and accuracies in differentiating polyp types, and its use is further hindered by inadequate representation of sessile serrated lesions and a lack of rigorous trials comparing it with standard colonoscopy.
conclusionDespite the emerging evidence of AI-assisted colonoscopy, its potential drawbacks and limitations may hinder the further implementation in real-world clinical practice. Long-term data on clinical efficacy, cost-effectiveness, liability, and data sharing are the key areas to be addressed.
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.