Role of Artificial Intelligence in Colonoscopy: A Literature Review of the Past, Present, and Future Directions

Colonoscopy remains one of the most common procedures performed by gastroenterologists and is critical for early detection and management of precursors to colorectal cancer (CRC). Although CRC remains one of the deadliest malignancies, earlier detection of precancerous polyps is directly associated with increased patient survival. As such, quality metrics for colonoscopy, such as polyp detection and mucosal visualization, are key parameters that are directly tied to patient outcomes. Over the past 2 decades, artificial intelligence and machine learning (AI/ML) tools have been tested and developed to augment colonoscopy performance and in 2021 resulted in the first-ever FDA-approved computer-aided detection (CADe) tool. This narrative review begins by reviewing the evidence behind the use of CADe that led to FDA approval. Next, the review discusses the current evidence and technological approaches for computer-aided diagnosis for optical in situ histopathological differentiation of colorectal polyps, including narrow-band imaging, blue light imaging, and endocytoscopy. Studies are ongoing to develop systems to predict the depth of submucosal invasion and to assess endoscopic disease activity among patients with inflammatory bowel disease. The applications of AI/ML to quality improvement are explored, including real-time assessment of bowel preparation, detection of cecal intubation, and automated polyp reporting and surveillance recommendations using natural language processing. Despite initial cost concerns, models have suggested that CADe systems could result in long-term cost savings and are generally accepted by patients and gastroenterologists. There is some reservation in adopting computer-aided diagnosis systems among gastroenterologists due to medico-legal concerns. Future directions for AI/ML in colonoscopy include health system improvements, such as automating note writing, optimizing procedural scheduling, and predicting sedation needs.