Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges

Abstract

Artificial intelligence (AI) is reshaping modern medicine, and gastroenterology and hepatology are among the specialties where its impact is becoming increasingly evident. AI has demonstrated the ability to process and analyze large amounts of clinical, radiological, endoscopic, and multi-omics data, offering unprecedented opportunities to enhance diagnostic accuracy, optimize therapeutic decision-making, and reduce variability in clinical practice. In endoscopy, computer-aided detection and diagnosis systems have shown consistent improvements in adenoma detection rates and real-time polyp characterization, while in hepatology, machine learning models outperform traditional scores for non-invasive assessment of liver fibrosis. Furthermore, multimodal approaches integrating genomics, microbiome, and imaging data are paving the way for precision medicine in inflammatory bowel disease and other complex digestive conditions. Despite these promising advances, significant barriers remain. The quality and heterogeneity of training data, the lack of rigorous external validation, and the opaque “black box” nature of many algorithms limit their clinical reliability. Ethical challenges, including accountability in case of diagnostic errors, protection of patient privacy, cost, and equitable access, also need to be addressed. This narrative review summarizes the current applications of AI in gastroenterology and hepatology, critically examines methodological and ethical challenges, and outlines future perspectives. Responsible, transparent, and equitable implementation will be essential for AI to transition from an emerging promise to a consolidated tool that improves outcomes and advances personalized digestive care.

Keywords: Artificial intelligence; Machine learning; Deep learning; Natural language processing; Large language models; Neurogastroenterology; Hepatology; Inflammatory bowel disease; Endoscopy

Core Tip: Artificial intelligence (AI) is rapidly transforming gastroenterology and hepatology, offering tools that enhance diagnostic precision, personalize treatment, and reduce variability in clinical practice. Applications such as computer-aided detection in colonoscopy, non-invasive assessment of liver fibrosis, and predictive modeling in inflammatory bowel disease illustrate its tangible clinical impact. However, the widespread adoption of AI remains constrained by methodological barriers, data quality issues, lack of external validation, and ethical challenges related to transparency, accountability, and equitable access. This review provides a comprehensive overview of benefits, limitations, and future perspectives, emphasizing the importance of responsible and patient-centered integration of AI.