Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma

Table 6 Barriers and potential solutions for clinical translation of artificial intelligence in advanced hepatocellular carcinoma

Limitation	Impact	Potential solution
Overfitting/low EPV	Model instability	Regularization, larger datasets
Limited external validation	Poor generalizability	Multicenter validation
Lack of causal modeling	Misinterpretation of treatment benefit	Counterfactual ML, uplift modeling
Poor calibration	Inaccurate risk estimation	Calibration plots, Brier score
Radiomic variability	Reduced reproducibility	Standardization, ComBat harmonization
Black-box models	Low clinical trust	Explainable AI
Lack of prospective validation	Limited clinical adoption	Prospective trials

EPV: Events per variable; ML: Machine learning; AI: Artificial intelligence; ComBat: Empirical Bayes-based batch effect harmonization method.