Artificial intelligence and personalised medicine in liver cancer

Primary liver cancers, including hepatocellular carcinoma and cholangiocarcinoma, represent a growing global health burden marked by rising incidence and high mortality. Clinical management requires integration of tumour stage, liver function, and patient-related factors to guide treatment decisions, yet current frameworks capture only a fraction of underlying disease complexity. In this setting, artificial intelligence (AI) is emerging as a key enabler of precision medicine in liver oncology. AI encompasses machine learning and deep learning approaches capable of identifying complex, non-linear patterns within large, high-dimensional datasets. These methods are increasingly applied across multimodal data inputs, including electronic health records, radiologic imaging, digital pathology, and omics profiling. In imaging, AI supports surveillance, lesion detection, characterisation, and prediction of recurrence, survival, and treatment response. In computational pathology, models are extending tissue analysis beyond description of morphology and marker expression toward prognostic modelling and inference of tumour biology. Integration of clinical, imaging, and pathological data in multimodal AI frameworks has shown superior performance compared with single-modality models for key outcomes such as recurrence and survival. Despite promising results, clinical translation remains limited by insufficient external and prospective validation, limited interpretability of complex models, and regulatory and workflow integration challenges. Addressing these barriers through federated learning strategies and the development of transparent, explainable systems is essential. If successfully implemented, AI-driven multimodal decision-support systems could substantially refine diagnosis, prognostication, and treatment selection in liver cancer, advancing personalised care in this biologically and clinically complex disease.