Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.

Abstract

Artificial intelligence (AI), which includes machine learning and deep learning, is fundamentally changing public health in gastroenterology and hepatology-fields grappling with a significant global disease burden. This review focuses on the population-level applications and impact of AI, highlighting its role in shifting healthcare strategies from reactive treatment to proactive prevention. AI demonstrates substantial improvements across many different areas. In colorectal cancer, AI models significantly boost detection rates, successfully identifying a large majority of high-risk individuals often missed by traditional screening methods. For metabolic dysfunction-associated steatotic liver disease (MASLD), advanced non-invasive tests offer a high degree of reliability in detecting liver fibrosis. The identification of viral hepatitis is enhanced with excellent accuracy, and gastrointestinal infection surveillance benefits from wastewater analysis that provides an early warning system weeks ahead of clinical case reporting. Furthermore, AI improves the diagnosis of upper GI cancers, such as gastric cancer, with higher diagnostic capability, and facilitates precision public health in inflammatory bowel disease (IBD) through highly accurate risk prediction models. Despite these important advances, significant hurdles remain. Key challenges include ensuring diverse and representative data to prevent algorithmic bias, protecting patient privacy, establishing robust regulatory frameworks for new technologies, and successfully moving innovations from research settings into practical, real-world deployment. The unequal distribution of AI development and access between high-income countries and low- and middle-income countries risks exacerbating existing health disparities. To fully realize AI's transformative potential for global public health in gastroenterology and hepatology, these cross-cutting issues must be actively addressed through ethical design, rigorous validation, and equitable worldwide deployment. [Abstract copyright: © 2025. The Author(s).]