An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models

The integration of digital health technologies, open-access data, and artificial intelligence (AI) is reshaping oncology by enabling more precise and personalized care. This review provides a focused update on AI, radiomics, and data integration in the context of liver oncology, with hepatocellular carcinoma (HCC) and colorectal liver metastases (CRLM) serving as key case models. Through multimodal datasets-including imaging, molecular profiles, and clinical records-AI and machine learning (ML) have demonstrated significant potential in improving early detection, risk stratification, and treatment response prediction in hepatic malignancies. Radiomics-driven tools have enabled non-invasive assessment of tumor biology, microvascular invasion, and therapeutic outcomes, particularly in HCC and CRLM. While applications in breast, lung, and non-metastatic colorectal cancers are briefly referenced for comparison, the central emphasis is on liver tumors as a representative field where AI-enabled precision oncology is rapidly advancing. Practical and ethical challenges surrounding clinical integration are also discussed, positioning liver oncology as a translational model for broader innovation in cancer care.