ReviewInternational journal of molecular sciences2026
The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.
Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colonoscopy is a key screening method for colorectal cancer (CRC), but its effectiveness is limited. Computer-aided detection (CADe) and computer-aided diagnostics (CADx), as part of an artificial intelligence (AI) system, improve the detection and optical characterization of lesions. This review maps and analyzes the evidence on the application of AI in colonoscopy, with a focus on the detection, segmentation, and characterization of colon neoplasms, available platforms, architectural models and implementation. The review was conducted in accordance with JBI and PRISMA-ScR guidelines, using the PCC framework. Meta-analyses, randomized controlled trials, systematic and narrative reviews, observational studies, guidelines, and consensus documents on the use of AI systems in different phases of colonoscopy were searched. CADe significantly improves adenoma detection and reduces the number of missed lesions. CADx, segmentation, depth of invasion assessment, and detection of learned lesions remain limited and heterogeneous. CADe has strong evidence for improving ADR, whereas current evidence for CADx remains insufficient to support a "resect-and-discard" strategy. Further cost-effectiveness studies are needed. Commercial platforms vary in their features and level of clinical validation. Colonoscopy using AI is a current topic with rapid development. This scoping review comprises heterogeneous literature covering clinical applications, technical aspects, the potential benefits and limitations of AI in improving colonoscopy performance and reducing the burden of colorectal cancer. Further trials involving diverse patient populations across different countries are needed to validate and extend the current evidence.
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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.