Artificial intelligence in the interventional management of liver disease: a narrative review from foundational concepts to clinical applications

Interventional radiology (IR) is an ideal domain for artificial intelligence (AI) due to its data-intensive nature. This review provides a targeted guide for clinicians on AI applications in liver interventions, specifically focusing on hepatocellular carcinoma and portal hypertension. Key findings from recent literature demonstrate that AI models achieve high accuracy in predicting the response to transarterial chemoembolization and in non-invasively estimating the hepatic venous pressure gradient. Furthermore, emerging deep learning architectures, such as Swin Transformers, are outperforming traditional mRECIST criteria in longitudinal treatment monitoring. Despite these technical successes, the transition from “code to bedside” is hindered by limited external validation and the “black box” nature of complex algorithms. We conclude that the future of IR lies in the “AI-augmented” interventional radiologist paradigm, in which AI serves as a precision tool for patient selection and procedural safety rather than as a replacement for clinical judgment.