Journal of hepatocellular carcinoma
Multimodal AI in Hepatocellular Carcinoma: Bridging Diagnosis, Therapy and Prognosis Toward Precision Oncology
By Chen X, Li F, Xu Y and 3 others.
Abstract
Background
This narrative review summarizes recent advances in multimodal AI for hepatocellular carcinoma (HCC), a highly heterogeneous malignancy. Traditional singlemodal data (eg imaging, serology, pathology) are insufficient for accurate diagnosis and personalized treatment. Multimodal technology, which integrates four categories of data (imaging, pathology, molecular, and clinical) through artificial intelligence (AI), has emerged as a promising solution. This review aims to summarize the sources, characteristics, and standardization of multimodal data in HCC, and to comprehensively update its clinical applications in diagnosis, treatment decision-making, and prognosis prediction.
Methods
We searched the literature in PubMed, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI) for articles on multimodal AI applications in HCC published up to 2025. We also critically appraised the included studies based on study design (retrospective vs prospective), validation strategies (internal vs external), sample size, multicenter status, and risk of bias (eg data leakage, overfitting, class imbalance).