Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice

Abstract

Artificial intelligence (AI) is driving a paradigm shift in gastroenterology and hepatology by delivering cutting-edge tools for disease screening, diagnosis, treatment, and prognostic management. Through deep learning, radiomics, and multimodal data integration, AI has achieved diagnostic parity with expert clinicians in endoscopic image analysis (<i>e.g.</i>, early gastric cancer detection, colorectal polyp identification) and non-invasive assessment of liver pathologies (<i>e.g.</i>, fibrosis staging, fatty liver typing) while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and optimizing inflammatory bowel disease treatment responses. Despite these advancements challenges persist including limited model generalization due to fragmented datasets, algorithmic limitations in rare conditions (<i>e.g.</i>, pediatric liver diseases) caused by insufficient training data, and unresolved ethical issues related to bias, accountability, and patient privacy. Mitigation strategies involve constructing standardized multicenter databases, validating AI tools through prospective trials, leveraging federated learning to address data scarcity, and developing interpretable systems (<i>e.g.</i>, attention heatmap visualization) to enhance clinical trust. Integrating generative AI, digital twin technologies, and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI's potential to redefine precision care for digestive disorders, improve global health outcomes, and reshape healthcare equity.

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