ReviewWorld journal of gastrointestinal endoscopy2026
Harnessing artificial intelligence in gastrointestinal endoscopy for early detection of dysplastic lesions in inflammatory bowel disease.
Review in World journal of gastrointestinal endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Machine Learning for Colitis-Associated Cancer in Inflammatory Bowel Disease: Evidence and Future Directions Toward Precision Medicine, a Narrative Review.International journal of molecular sciences · 2026Review
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
Artificial intelligence (AI) has become an increasingly valuable tool in gastrointestinal endoscopy, especially in the context of inflammatory bowel disease, including ulcerative colitis and Crohn's disease. One of its most impactful applications is the early detection and accurate characterization of dysplastic lesions, which are often difficult to identify owing to chronic inflammation and mucosal alterations. AI-based systems, such as computer-aided detection and computer-aided diagnosis, have shown excellent performance in identifying subtle or flat lesions, improving the quality and consistency of dysplasia surveillance, and aiding in colorectal cancer prevention. In addition to neoplasia detection, AI plays a significant role in assessing mucosal inflammation and disease activity by offering objective scoring, reducing interobserver variability, and even predicting histological remission without the need for biopsy. These capabilities enhance decision-making and support a more personalized approach to patient management. While technical, regulatory, and integration challenges remain, current evidence supports the growing role of AI in improving both diagnostic precision and long-term outcomes in inflammatory bowel disease endoscopy.
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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.