Artificial intelligence-driven risk estimation, early detection, diagnostic characterisation and prognosis in hepatitis b-related hepatocellular carcinoma

Table 5 Present-day gaps and translational priorities

Domain	Current limitation	Recommended priority
Study design	Predominantly retrospective, single-centre	Prospective, multicentre, outcome-based designs
Aetiological specificity	HBV-specific evidence a minority of published work	Dedicated HBV cohorts rather than extrapolation from mixed or HCV populations
Validation	Internal validation dominant; external scarce	Mandatory external, temporal and geographic validation
Comparison	Superiority inferred across dissimilar studies	Head-to-head comparison against named scores within a single validation cohort
Calibration	Inconsistently reported	Routine calibration alongside discrimination
Explainability	Variable use of interpretability methods	Standardised Shapley-based explanation
Reporting	Uneven adherence to standards	TRIPOD+AI and PROBAST+AI as publication conditions
Equity	Cohorts skewed to East Asian and European data	Inclusion of African and South Asian HBV populations
Data scale	Siloed institutional datasets	Federated learning across centres

HBV: Hepatitis B virus; HCV: Hepatitis C virus; PROBAST+AI: Prediction model risk of bias assessment tool, artificial intelligence extension; TRIPOD+AI: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis, artificial intelligence extension.