ReviewClinical endoscopy2021
Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective.
Review in Clinical endoscopy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Endoscopists' attitudes toward and experiences with artificial intelligence-assisted colonoscopy: a 3-year follow-up survey after the implementation of GI Genius.Annals of coloproctology · 2026Article
- Automatic metrics from a new colon phantom can ensure basic competency in colonoscopy.Surgical endoscopy · 2026Observational
- Re-investigation rate following colon capsule endoscopy stratified by faecal haemoglobin concentration in a screening population.Therapeutic advances in gastroenterology · 2026Article
- Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes.World journal of gastroenterology · 2025Review
- GI Genius increases small and right-sided adenoma and sessile serrated lesion detection rate when used with EndoCuff in a real-world setting: a retrospective United States study.Clinical endoscopy · 2025Article
- Improvement in adenoma detection rate by artificial intelligence-assisted colonoscopy: Multicenter quasi-randomized controlled trial.Endoscopy international open · 2025Article
- Review
- Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center.Endoscopy international open · 2023Article
- Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials.Clinical endoscopy · 2023Review
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Review
- Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy.Clinical endoscopy · 2022Review
- Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images.Journal of personalized medicine · 2022Article
- No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification.Journal of personalized medicine · 2022Article
- Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases.Sensors (Basel, Switzerland) · 2022Article
- Innovation in Gastroenterology-Can We Do Better?Biomimetics (Basel, Switzerland) · 2022Article
- Artificial Intelligence-Based Colorectal Polyp Histology Prediction: High Accuracy in Larger Polyps.Clinical endoscopy · 2022Article
- Predictors of Positive Video Capsule Endoscopy Findings for Chronic Unexplained Abdominal Pain: Single-Center Retrospective Study and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2021Article
- Application of artificial intelligence in gastrointestinal endoscopy in Vietnam: a narrative review.Therapeutic advances in gastrointestinal endoscopyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present manuscript aims to review the history, recent advances, evidence, and challenges of artificial intelligence (AI) in colonoscopy. Although it is mainly focused on polyp detection and characterization, it also considers other potential applications (i.e., inflammatory bowel disease) and future perspectives. Some of the most recent algorithms show promising results that are similar to human expert performance. The integration of AI in routine clinical practice will be challenging, with significant issues to overcome (i.e., regulatory, reimbursement). Medico-legal issues will also need to be addressed. With the exception of an AI system that is already available in selected countries (GI Genius; Medtronic, Minneapolis, MN, USA), the majority of the technology is still in its infancy and has not yet been proven to reach a sufficient diagnostic performance to be adopted in the clinical practice. However, larger players will enter the arena of AI in the next few months.
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.