Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges

Table 2 Application of artificial intelligence in hepatocellular carcinoma diagnosis

Management stage	Data source/technology	AI application	Key benefit	Evidence level	Primary limitation
Risk stratification	Clinical, viral (HCV), cirrhosis data	ML models (outperform traditional regression)	Superior prediction of HCC risk, even post-HCV eradication or in MASLD	Research	Retrospective design; single center; limited generalizability; data heterogeneity; 'black box' nature
Imaging diagnosis	Radiomics (CT, MRI)/DL	Classification of benign vs malignant lesions	Identifies indeterminate nodules and predicts MVI	Research	Validation needed in diverse populations; 'black box' interpretability; generalizability across centers
Pathology	H&E histological slides	DL/CNNs	Classification of tumor subtypes and prediction of genetic mutations with near-perfect reliability	Research	Dependence on quality of scanned slides; potential for algorithmic bias; 'black box' interpretability
Prognosis/treatment	Imaging/multi-omics/clinical data	Estimation of survival and therapeutic response	Prediction of recurrence and response to locoregional therapies (e.g., TACE)	Research	Need for broader multicenter validation; data heterogeneity; 'black box' nature; integration into clinical workflow

HCV: Hepatitis C virus; ML: Machine learning; CT: Computerized tomography; MRI: Magnetic resonance imaging; DL: Deep learning; MVI: Microvascular invasion; H&E: Hematoxylin and eosin; CNN: Convolutional neural networks; TACE: Transarterial chemoembolization; AI: Artificial intelligence; HCC: Hepatocellular carcinoma.