Internal medicine

Radiology

Pathology

Colonoscopy

Colorectal cancer

Cancer

Engineering

Artificial intelligence

Medical physics

Engineering drawing

Adenoma

Endoscopy

Computer science

Polypectomy

Medicine

Digestive Endoscopy

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 &gt;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 &gt;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.

Artificial intelligence and colonoscopy: Current status and future perspectives