Revolutionizing Liver Imaging: Artificial Intelligence-Driven Advances in Diagnostics and Staging.

Artificial intelligence (AI) has emerged as a transformative tool in liver imaging, offering enhanced diagnostic accuracy, efficiency, and reproducibility. The integration of machine learning and deep learning algorithms into radiological workflows has shown significant promise across a wide range of liver diseases. Key applications include automated liver segmentation on computed tomography (CT) and magnetic resonance imaging (MRI), enabling accurate liver volumetry and lesion localization. In metabolic dysfunction-associated steatotic liver disease, AI facilitates the detection and quantification of hepatic steatosis using advanced image analysis on ultrasound, CT, and MRI, providing a non-invasive alternative to biopsy. AI algorithms also demonstrate strong performance in detecting, classifying, and characterizing focal liver lesions such as hemangioma, focal nodular hyperplasia, hepatocellular carcinoma (HCC), and metastases, improving lesion conspicuity, standardizing reporting through LI-RADS, and reducing inter-observer variability. Beyond diagnosis, AI is increasingly applied for risk stratification and prognostication in HCC, integrating imaging, clinical, and laboratory data to predict tumor development, aggressiveness, treatment response, and survival outcomes. Despite these advances, the clinical implementation of AI in liver imaging faces notable challenges such as the need for data harmonization across scanners and institutions, rigorous validation in diverse patient populations, regulatory approval, and ethical considerations surrounding patient privacy, algorithmic bias, and transparency. Addressing these limitations through robust research, multi-center studies, and carefully designed clinical integration strategies is essential to safely and effectively harness AI's potential. With continued development and validation, AI has the capacity to enhance diagnostic workflows, enable precision medicine, and ultimately improve patient outcomes in hepatology.