Virtual Reality and Artificial Intelligence in Gastrointestinal Endoscopy

Abstract

Background: Three distinct technological concepts are increasingly applied in gastrointestinal endoscopy: 1) virtual reality simulation for training; 2) immersive head-mounted display visualization of live endoscopic video; and 3) artificial intelligence-assisted computer-aided detection. Each technology has a separate evidence base and clinical application. Objective: To review the current evidence for VR simulation, immersive visualization, and AI-assisted endoscopy; examine training applications; and discuss implementation considerations for emerging healthcare systems, with a focus on Kenya. Methods: Comprehensive review of peer-reviewed literature from 2020-2025, including meta-analyses, randomized controlled trials, and clinical studies. PubMed, MEDLINE, and Cochrane Library were searched using the terms “AI-assisted colonoscopy”, “computer-aided detection endoscopy”, “virtual reality endoscopy simulation”, and “immersive visualization endoscopy”. Evidence levels were assessed according to the Oxford Centre for Evidence-Based Medicine framework. Studies were evaluated independently for each technology to avoid conflation of evidence bases. Results: AI-assisted colonoscopy has demonstrated reduced adenoma miss rates in randomized trials (relative reduction up to 55% in meta-analyses). VR simulation training improves technical skills and reduces patient discomfort during trainee-performed procedures. Immersive visualization platforms may enhance spatial perception but currently lack direct clinical outcome trials. Conclusion: AI-assisted endoscopy has established clinical evidence for improved detection. VR simulation is effective for training. Immersive visualization requires further clinical evaluation. These technologies may influence future endoscopic practice, but implementation in resource-limited settings requires careful consideration of infrastructure, cost, and training requirements. Note: This manuscript does not present original primary data from clinical settings in Kenya. The Kenya-focused content comprises a narrative implementation framework and a cost-estimation model derived from published literature and publicly available health economic data. No patient cohort, control group, or prospectively collected dataset from Kenya is included.