Artificial intelligence and colonoscopy: Current status and future perspectives
Abstract
Background and Aim: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer‐aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. Methods and Results: Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. Conclusion: We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence‐assisted medical devices.
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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 [19]
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- Authors
- Kudo, S., Mori, Y., Misawa, M., Takeda, K., Kudo, T., Itoh, H., Oda, M., & Mori, K.
- Affiliations
- SHOWA Medical University Northern Yokohama Hospital (JP)
- Nagoya University (JP)
- Corresponding author
- Kudo, Shin‐ei
- Venue
- Digestive Endoscopy
- Publisher
- Wiley
- Published
- 2019-02-27
- Volume
- 31
- Issue
- 4
- Pages
- 363-371
- ISSN
- 0915-5635 (ISSN-L)
- 1443-1661
- OpenAlex
- W2909293781
- Cited by
- 147
- References
- 66
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 10 · 2023: 20 · 2022: 18 · 2021: 30 · 2020: 30 · 2019: 13 · 2014: 1
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Digestive Endoscopy (publishedVersion)
- PubMed (publishedVersion)
- Funders
- American Society for Gastrointestinal Endoscopy
- U.S. Food and Drug Administration
- Japan Society for the Promotion of Science (19KK0421, 19K08403)
- 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 diagnosis, polyp characterization, adenoma detection rate, optical biopsy, hyperplastic polyps, polypectomy, regulatory approval
- MeSH terms
- Artificial Intelligence, Colonic Polyps, Colonoscopy, Diagnosis, Computer-Assisted, Diagnostic Errors, Forecasting, Humans, Colorectal Neoplasms, Quality Improvement
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