ArticleiGIE : innovation, investigation and insights2025
Asia-Pacific consensus on the use of artificial intelligence in colorectal cancer screening and surveillance.
Article in iGIE : innovation, investigation and insights, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 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
- Computer-assisted detection of colorectal polyps: a narrative review of clinical utility, ongoing limitations, and opportunities for advancement.Translational gastroenterology and hepatology · 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
25 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Artificial intelligence (AI)-assisted colonoscopy has been widely investigated for colorectal adenoma and cancer detection and characterization. However, clinical guidance on its use in colorectal cancer (CRC) screening and surveillance is lacking. In this study, we developed consensus guiding when and how to use AI-assisted colonoscopy in the daily practice of screening and surveillance of colorectal neoplasia. Methods: Experts from 12 Asian-Pacific countries and regions together with 4 international experts developed a set of consensus statements based on existing clinical evidence using the modified Delphi process. Results: Based on existing evidence, computer-assisted detection should be evaluated for use if available and deemed cost-effective as an adjunct to conventional colonoscopy for CRC screening. Computer-assisted diagnosis may be used for characterization of polyps but is not yet considered sufficient to determine if a polyp is neoplastic and needs to be removed. Computer-assisted quality assurance is generally welcome to improve the quality of colonoscopy. Early engagement of patients and nurses and training of endoscopists to use AI-assisted colonoscopy are crucial for the successful implementation. Conclusions: More validation studies on AI-assisted colonoscopy need to be done while AI technologies continue to improve.
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.