To evaluate the methodological evolution of AI in hepatocellular carcinoma (HCC) from 2025 to 2026 and address the translational challenges in clinical applications.
Key Findings:
AI models show promise in enhancing HCC diagnostics but face significant translational barriers that limit their clinical application.
Complex AI architectures remain unvalidated in real-world clinical settings, raising concerns about their reliability.
Traditional Cox models outperform complex AI models in low-dimensional survival prediction, suggesting that complexity does not guarantee better outcomes.
Interpretation:
The findings suggest that while advanced AI techniques have the potential to improve HCC diagnostics, their practical utility is constrained by data dependency and a lack of validation across diverse clinical environments.
Limitations:
AI models are sensitive to variations in clinical reporting and imaging protocols, which can affect their performance.
Current AI applications often overfit to specific institutional datasets, limiting their generalizability.
Existing multicenter databases do not adequately address the generalization crisis in AI algorithms, impacting their clinical applicability.
Conclusion:
To enhance the clinical utility of AI in HCC, the field must urgently shift towards interpretable models and integrate these into rigorous clinical trials and regulatory frameworks.
