The application of artificial intelligence-based tools in the management of hepatocellular carcinoma: current status and future perspectives

Abstract

Artificial intelligence (AI) is rapidly advancing in hepatocellular carcinoma (HCC) management, offering promising applications across diagnosis, prognosis, and treatment. In histopathology, deep learning models have shown impressive accuracy in differentiating liver lesions and extracting prognostic information from tissue samples. For biomarker discovery, AI techniques applied to multi-omics data have identified novel prognostic signatures and predictors of immunotherapy response. In radiology, convolutional neural networks have demonstrated high performance in classifying hepatic lesions, grading tumors, and predicting microvascular invasion from computed tomography (CT) and magnetic resonance imaging (MRI) images. Multimodal AI models integrating histopathology, genomics, and clinical data are emerging as powerful tools for risk stratification. Large language models (LLMs) show potential to support clinical decision making and patient education, though concerns about accuracy remain. While AI holds immense promise, several challenges must be addressed, including algorithmic bias, data privacy, and regulatory compliance. The successful implementation of AI in HCC care will require ongoing collaboration between clinicians, data scientists, and ethicists. As AI technologies continue to evolve, they are expected to enable more personalized approaches to HCC management, potentially improving diagnosis, treatment selection, and patient outcomes. However, it is crucial to recognize that AI tools are designed to assist, not replace, clinical expertise. Continuous validation in diverse, real-world settings will be essential to ensure the reliability and generalizability of AI models in HCC care.