Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases

Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobiliary and hepatic conditions. A key focus is differentiating between benign and malignant lesions, which is crucial for treatment decisions. AI improves diagnostic accuracy through high sensitivity and specificity, while CNN algorithms enhance image analysis and reduce variability. These advancements aim to match the accuracy of pathologists in cancer detection. In addition, AI aids in identifying diagnostic markers, as early detection is essential. This article reviews the applications of machine learning and deep learning in the diagnosis of hepatic and pancreaticobiliary diseases. Although the use of AI in these specialized areas of gastroenterology is primarily confined to experimental trials, current models demonstrate significant potential for enhancing the detection, evaluation, and treatment planning of hepatic and pancreaticobiliary conditions.