Foundation Models (FMs) are profoundly transforming the clinical management pathway for liver cancer. Their core value lies in enhancing diagnostic accuracy, enabling personalized therapeutic decision-making, and optimizing clinical efficiency through the integrative analysis of multimodal data, encompassing symptoms, medical histories, imaging findings, and genomic profiles. At the diagnostic level, FMs facilitate risk stratification by analyzing symptom and historical data while leveraging imaging features to aid in the early identification of lesions. Within the therapeutic decision-making domain for liver cancer, intelligent systems such as IDEAL provide critical clinical support by enabling precise hepatic anatomical reconstruction, quantitative assessment of surgical feasibility, and generation of individualized therapeutic recommendations for targeted agents and immunotherapy, thereby serving as essential adjuncts to HCC management. Despite their transformative potential, the clinical deployment of FMs faces significant core challenges: including concerns over model accuracy, reliability, and ethical issues such as data privacy and equitable access. Addressing these challenges requires interdisciplinary efforts focused on domain-specific model fine-tuning, robust ethical frameworks, and standardized regulatory guidelines. This review outlines the evolution and current state of FMs within healthcare, specifically highlighting their substantial application value in the liver cancer therapeutic landscape, and articulates the stage-specific challenges impeding their broader clinical adoption.
Wang et al. (2026) studied this question.