Artificial intelligence in colonoscopy: A review on the current status
Open accessAbstract
Artificial intelligence has become an increasingly hot topic in the last several years, and it has also gained its way into the medical field. In recent years, the application of artificial intelligence in the gastroenterology field has been of increasing interest, particularly in the colonoscopy setting. Novel technologies such as deep neural networks have enabled real‐time computer‐aided polyp detection and diagnosis during colonoscopy. This might lead to increased performance of endoscopists as well as potentially reducing the costs of unnecessary polypectomies of hyperplastic polyps. Newly published prospective trials studying computer‐aided detection showed that the assistance of artificial intelligence significantly increased the detection of polyps and non‐advanced adenomas approximately by 10%, while three tandem randomized control trials proved that the adenoma miss rate was significantly reduced (e.g., 13.8% vs. 36.7% in one Japanese multicenter trial). Promising results have also been shown in prospective single‐arm trials on computer‐aided polyp diagnosis, but the evidence is insufficient to reach a conclusion.
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Previous studies on CADx systems for colonoscopy focused on assessing the performances of AI against the accuracy of physicians or against criteria established by gastroenterology societies for implementing the technology in a clinical setting.
“Previous studies on cadx systems for colonoscopy focused on assessing the performances of ai against the accuracy of physicians19,20,27–30, or against the criteria established by gastroenterology societies for implementing the technology in a clinical setting31–34.”
From Experimental evidence of effective human–AI collaboration in medical decision-making (2022) · cites this paper as [20]
The ‘Computer-aided polyp diagnosis’ section (page unavailable) reports a comparison of CADx accuracy with observers’ diagnoses and describes a study assessing whether CADx met the NPV ≥ 90% threshold for a diagnose-and-leave strategy. It does not identify that threshold as a criterion established by a gastroenterology society or describe it as a general criterion for clinical implementation. (LLM, conf=0.84)
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- Authors
- Larsen, S. L. V., & Mori, Y.
- Affiliations
- Oslo University Hospital (NO)
- Corresponding author
- Mori, Yuichi
- ORCID
- Mori, Yuichi
- Venue
- DEN Open
- Publisher
- Wiley
- Published
- 2022-03-23
- Volume
- 2
- Issue
- 1
- Pages
- e109-e109
- ISSN
- 2692-4609 (ISSN-L)
- PMC
- PMC9302306
- OpenAlex
- W4220704330
- Cited by
- 21
- References
- 34
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 4 · 2023: 4 · 2022: 2
- FWCI
- 0.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- DEN Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, colonoscopy, computer-aided detection, polyp detection, adenoma detection rate, deep neural network, hyperplastic polyps, polypectomy, endoscopy
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