Artificial intelligence in gastroenterology: Enhancing clinical practice, managing challenges and exploring future directions

Artificial intelligence (AI) is transforming gastroenterology by enhancing diagnostic accuracy, enabling personalized treatment, and improving disease management efficiency. This review explored the evolution and application of core AI technologies, including machine learning, deep learning, and neural networks, that underpin modern computational advancements in the field. These tools have demonstrated significant success in detecting premalignant and malignant lesions and in managing gastrointestinal bleeding, colorectal cancer, and Helicobacter pylori infection. AI also supports the diagnosis and treatment of liver and pancreatic diseases. Its use is expanding in functional gastrointestinal disorders such as irritable bowel syndrome with emerging applications in pediatric gastroenterology. In addition AI enables advanced risk stratification and addresses persistent challenges in conventional diagnostic and therapeutic approaches, including interobserver variability and inefficiencies in care delivery. However, integration into routine clinical practice faces several barriers, including data privacy concerns, algorithmic bias, limited model interpretability, regulatory gaps, and interoperability issues with existing healthcare infrastructure. Future directions include real-time procedural guidance, multi-omic prediction models, minimally invasive surgical automation, and drug discovery. Achieving the full potential of AI will require ethical governance, regulatory clarity, and sustained interdisciplinary collaboration.