The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility

Synthesizing recent literature from 2025 to 2026, this mini-review critically evaluates the methodological evolution of artificial intelligence (AI) in hepatocellular carcinoma (HCC) from static pattern recognition to mechanism-driven spatial and imaging diagnostics. We expose a critical translational paradox: although self-supervised Vision Foundation Models (VFMs), unsupervised domain adaptation for H&E-based virtual molecular profiling, and AI-synergized spatial transcriptomics theoretically decode intratumoral heterogeneity and mitigate cross-etiology domain shifts, these complex architectures remain unvalidated in real-world multi-scanner cohorts. Conversely, robust comparative analyses challenge the prevailing multi-modal narrative by demonstrating that algorithmic utility is highly data-dependent: although complex AI is crucial for high-dimensional settings, traditional Cox models remain highly robust and competitive compared to highly engineered large language models (LLMs) in low-dimensional survival prediction, indicating that algorithmic complexity does not inevitably equal clinical utility. To break this translational impasse, this mini-review argues that the field must urgently pivot away from fragile <i>post-hoc</i> explainability toward inherently interpretable architectures-such as concept bottleneck models (CBMs)-and calls for the rigorous integration of these transparent systems into Phase II "window-of-opportunity" randomized trials and dynamic regulatory evaluation frameworks within the next 24 months.