ReviewGastro hep advances2025
Artificial Intelligence-Assisted Endoscopy in Diagnosis of Gastrointestinal Tumors: A Review of Systematic Reviews and Meta-Analyses.
Review in Gastro hep advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Artificial Intelligence for the Detection of Small Bowel Lesions and Neoplasia: A Scoping Review.Cureus · 2026Review
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Artificial intelligence assistance improves endoscopist accuracy for gastric cancer dysplasia and intestinal metaplasia.Scientific reports · 2026Article
- CostWorld journal of gastrointestinal oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI)-assisted endoscopy has emerged as a promising tool for early and accurate detection of gastrointestinal (GI) tumors, which are associated with high morbidity, mortality, and financial burden. This review summarizes systematic reviews and meta-analyses on AI-assisted endoscopy in GI tumor diagnosis. A comprehensive search was conducted using PubMed/MEDLINE, Google Scholar, Directory of open Access Journals, African Journals Online, and the Cochrane Library, supplemented by manual searches. Eligible systematic reviews and meta-analyses were selected based on predefined inclusion criteria, and relevant data were extracted to evaluate AI-assisted endoscopy's diagnostic performance. Out of 569 identified studies, 23 systematic reviews with meta-analyses met the inclusion criteria, with 6 focusing on detection rates and 17 on diagnostic accuracy. AI-assisted endoscopy demonstrated a significantly higher detection rate for GI tumors compared to conventional endoscopy, alongside high diagnostic accuracy across different GI tumor types. However, variability in performance was observed among different AI algorithms and studies. While AI-assisted endoscopy enhances diagnostic precision, rigorous validation of AI models is necessary to ensure clinical reliability. Ethical considerations and further research are crucial for optimizing AI's role in health care.
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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.