Clinical validation of artificial intelligence for gastrointestinal diseases

Artificial intelligence (AI), particularly deep convolutional neural networks (DCNNs), has demonstrated significant potential for transforming the diagnosis and management of gastrointestinal (GI) diseases. This review critically examines the evolving landscape of AI applications in gastroenterology, moving beyond a simple catalog of tools to analyze their pathway to clinical integration. We synthesize current evidence across key functional domains including computer-aided detection (CADe), computer-aided diagnosis (CADx), and predictive outcome modeling highlighting performance metrics and early clinical adoption. Crucially, we identify a pronounced translational gap between technical validation and demonstrable improvement in patient-centered outcomes. The narrative underscores that while AI systems show high diagnostic accuracy in controlled studies, their ultimate clinical utility remains unproven. The conclusion distills core challenges including the need for rigorous multicenter randomized trials, solutions for algorithmic generalizability, and effective human-AI collaboration and emphasizes the urgent imperative for structured clinical validation frameworks to realize AI’s promise in routine GI care. We further synthesize evidence by validation stage and study design, highlighting clinical endpoints such as ADR, APC, and complication rates, with GI Genius and ENDOANGEL exemplifying the gap between technical metrics and patient outcomes.