ReviewCancers2023
Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AGA Living Clinical Practice Guideline on Computer-Aided Detection-Assisted Colonoscopy.Gastroenterology · 2025Guideline
- Article
- Emerging Endorobotic and AI Technologies in Colorectal Cancer Screening: A Review of Design, Validation, and Translational Pathways.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes.World journal of gastroenterology · 2025Review
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review.Cureus · 2025Review
- Explaining the Correlates of the Multi-Theory Model (MTM) of Health Behavior Change in Visual (Structural) Colorectal Cancer Screening Examinations.International journal of environmental research and public health · 2025Article
- Impact of Artificial Intelligence on Polyp Size and Surveillance Colonoscopy: A Phantom Study.Cureus · 2024Article
- Development, deployment and scaling of operating room-ready artificial intelligence for real-time surgical decision support.NPJ digital medicine · 2024Article
- Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review.JMIR medical informatics · 2024Review
- Comparative Study of Predictive Models for the Detection of Patients at High Risk of Inadequate Colonic Cleansing.Journal of personalized medicine · 2024Article
- Colon Bowel Preparation in the Era of Artificial Intelligence: Is There Potential for Enhancing Colon Bowel Cleansing?Medicina (Kaunas, Lithuania) · 2023Review
- Computer-Based Diagnosis of Celiac Disease by Quantitative Processing of Duodenal Endoscopy Images.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.
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.