Artificial Intelligence for the Practicing Gastroenterologist: Foundations, Contemporary Applications, Real-World Constraints, and Future Directions

Abstract

Artificial intelligence (AI) has rapidly transitioned from an experimental concept into a clinically influential technology in gastroenterology, with applications spanning upper endoscopy, colonoscopy, inflammatory bowel disease, hepatology, pancreatobiliary, and office-based workflow. Gastroenterology now accounts for the largest share of randomized trials evaluating clinical AI in medicine, reflecting both the data-rich nature of gastrointestinal practice and the maturity of computer-aided detection and computer-aided diagnosis systems. In this narrative review, we summarize the foundational concepts and reporting standards relevant to AI evaluation. We highlight emerging applications beyond lesion detection such as bowel preparation prediction, lesion sizing, multimodal endoscopic–histologic fusion, MRCP+ in primary sclerosing cholangitis, deep-learning detection of pancreatic ductal adenocarcinoma on noncontrast computed tomography, and ambient documentation tools. We also appraise the limitations that constrain clinical translation, including alert fatigue, surveillance burden, algorithmic bias, generalizability, and the recently described risk of endoscopist deskilling. We propose that practicing gastroenterologists should not be intimidated by AI but understand that it is another computation tool based on probabilities, and therefore AI-related technologies should be critically reviewed and adopted to improve workflow and diagnostic skills rather than as an autonomous diagnostic substitute.