ReviewDiagnostics (Basel, Switzerland)2024
A Comprehensive Review of Artificial Intelligence and Colon Capsule Endoscopy: Opportunities and Challenges.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in endoscopy and colonoscopy: a comprehensive bibliometric analysis of global research trends.Frontiers in medicine · 2025Pooled it
- Robotic Capsule Endoscopy: Simultaneous Gastric and Enteric Evaluation in Real-World Practice.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-assisted versus conventional reading in pan-intestinal capsule endoscopy for suspected mid-lower gastrointestinal bleeding: a retrospective analysis of a prospective cohort.BMJ open gastroenterology · 2025Article
- Integration of Artificial Intelligence-Enhanced Capsule Endoscopy in Clinical Practice: A Review of Market-Available Tools for Clinical Practice.Digestive diseases and sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colon capsule endoscopy (CCE) enables a comprehensive, non-invasive, and painless evaluation of the colon, although it still has limited indications. The lengthy reading times hinder its wider implementation, a drawback that could potentially be overcome through the integration of artificial intelligence (AI) models. Studies employing AI, particularly convolutional neural networks (CNNs), demonstrate great promise in using CCE as a viable option for detecting certain diseases and alterations in the colon, compared to other methods like colonoscopy. Additionally, employing AI models in CCE could pave the way for a minimally invasive panenteric or even panendoscopic solution. This review aims to provide a comprehensive summary of the current state-of-the-art of AI in CCE while also addressing the challenges, both technical and ethical, associated with broadening indications for AI-powered CCE. Additionally, it also gives a brief reflection of the potential environmental advantages of using this method compared to alternative ones.
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.