ReviewDiagnostics (Basel, Switzerland)2023
Clinicians' Guide to Artificial Intelligence in Colon Capsule Endoscopy-Technology Made Simple.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 17 citations in OpenAlex.
- Expected Values of Artificial Intelligence in Colon Capsule Endoscopy: Artificial Intelligence in Capsule Endoscopy Consortium Position Statement.Endoscopy international open · 2026Article
- Could artificial intelligence-powered colonoscopies change the future of colorectal cancer screening?World journal of gastroenterology · 2025Review
- Article
- Introducing colon capsule endoscopy as a new diagnostic modality for patients with bowel symptoms in general practice: a feasibility study.BMC primary care · 2025Article
- Unifying terminology, reporting, and bowel preparation standards in colon capsule endoscopy: Nyborg Consensus.Endoscopy international open · 2025Article
- Evaluation of Colon Capsule Utilisation in Europe-CAPTURE EU Survey Findings.Journal of clinical medicine · 2024Article
- Colon capsule endoscopy: Can it contribute to green endoscopy?World journal of gastrointestinal endoscopy · 2024Article
- Polyp Matching in Colon Capsule Endoscopy: Pioneering CCE-Colonoscopy Integration Towards an AI-Driven Future.Journal of clinical medicine · 2024Review
- A Comprehensive Review of Artificial Intelligence and Colon Capsule Endoscopy: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2024Review
- The Diagnostic Accuracy of Colon Capsule Endoscopy in Inflammatory Bowel Disease-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- Robotic wireless capsule endoscopy: recent advances and upcoming technologies.Nature communications · 2024Review
- Rationalizing polyp matching criteria in colon capsule endoscopy: an international expert consensus through RAND (modified DELPHI) process.Therapeutic advances in gastroenterology · 2024Article
- Clinical utility of colon capsule endoscopy: a moving target?Therapeutic advances in gastroenterology · 2023Review
- Artificial intelligence in colonoscopy: where have we been and where should we go?Translational gastroenterology and hepatology · 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
8 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic's impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology's most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general "fear of the unknown in AI" by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.
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.