ArticleWorld journal of gastrointestinal endoscopy2024
Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy.
Article in World journal of gastrointestinal endoscopy, 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.
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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
- Gastrointestinal endoscopic image style transfer using EndoStyle to improve artificial intelligence prediction models.NPJ digital medicine · 2026Article
- Role of Artificial Intelligence in Identifying Vital Biomarkers with Greater Precision in Emergency Departments During Emerging Pandemics.International journal of molecular sciences · 2025Article
- Random forests algorithm using basic medical data for predicting the presence of colonic polyps.Frontiers in surgery · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundImproved adenoma detection rate (ADR) has been demonstrated with artificial intelligence (AI)-assisted colonoscopy. However, data on the real-world application of AI and its effect on colorectal cancer (CRC) screening outcomes is limited.
aimTo analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or symptoms.
methodsAI software (GI Genius, Medtronic) was implemented into the standard procedure protocol in November 2022. Data was collected on patient demographics, procedure indication, polyp size, location, and pathology. CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up.
resultsWe evaluated 1008 colonoscopies (278 pre-AI, 255 early post-AI, 285 established post-AI, and 190 late post-AI). The ADR was 38.1% pre-AI, 42.0% early post-AI (
conclusionIn patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy. Although the potential of AI in colonoscopy is undisputed, current AI technology may not universally elevate screening metrics across all situations and patient populations. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.
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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.