Advances in artificial intelligence for imaging-based diagnosis of hepatocellular carcinoma

Abstract

This minireview summarizes recent advances in artificial intelligence (AI) for imaging-based diagnosis of hepatocellular carcinoma (HCC), emphasizing how technical progress translates to clinical practice. Developments from convolutional neural networks to transformer-based, data-efficient, and spatiotemporal models have improved lesion detection, segmentation, classification, and treatment response assessment. These innovations underpin emerging clinical systems such as intelligent quantitative liver imaging analysis system and smart liver imaging analysis system, demonstrating growing translational impact. Yet persistent challenges-including cross-site generalizability, interpretable decision support, seamless workflow integration, and regulatory oversight-continue to shape research priorities in multimodal fusion, longitudinal modeling, and foundation model pretraining. Together, these efforts delineate the path toward trustworthy, clinically integrated AI in HCC imaging.

Keywords: Hepatocellular carcinoma; Imaging-guided diagnosis; Artificial intelligence; Deep learning; Clinical translation

Core Tip: Artificial intelligence (AI) for image-guided hepatocellular carcinoma diagnosis improves sensitivity to small lesions, enables standardized quantitative readouts, and reduces reporting time, thereby complementing conventional radiology. Built on diverse imaging datasets and evolving model designs, AI has advanced across detection, segmentation, characterization, and response assessment, with early clinical uptake. Outstanding obstacles-cross-institution generalization and clinically meaningful explainability-must be resolved to achieve scalable, trustworthy deployment.