Artificial intelligence-assisted colonoscopy: A review of current state of practice and research

Abstract

Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

Keywords: Colonoscopy; Adenoma; Artificial intelligence; Computational intelligence; Endoscopy; Surveillance

Core Tip: Artificial intelligence (AI)-assisted decision support systems for endoscopy have shown promising results for the detection and classification of colorectal lesions. However, their integration into clinical practice is currently limited by the lack of design, validation, and testing under real-life clinical conditions. Further work is required to address the challenges of AI integration, to navigate the regulatory approval process, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of accuracy and safety. This article describes the current state of AI integration into colonoscopy practice and offers suggestions for future research.