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

Table 1 Summary of artificial intelligence applications for non-invasive assessment of fibrosis and cirrhosis

Input data type	AI tool	Conventional metric/tool outperformed	Key AI contribution	Evidence level	Primary limitation
Clinical/Laboratory	RF, LightGBM, ANN	FIB-4 score and transient elastography	More reliable prediction of fibrosis stage; development of novel indices (e.g., FIB-6, validated in multicenter cohorts)	Moderate-high (multicenter validation available for FIB-6; large cohorts in MASLD studies) near clinical use	Some models remain retrospective; limited external validation for several algorithms; interpretability constraints
Imaging (CT, MRI, US, elastography)	DL (CNNs, ResNet50), Radiomics	Expert radiologists; elastography alone	Enhanced cirrhosis detection; automatic segmentation; identification of inflammation/fibrosis; distinction of etiologies; radiomics improves staging precision	Moderate (several studies with external validation, but heterogeneous datasets), promising	Data heterogeneity; many single-center cohorts; limited standardized imaging protocols; 'black box' interpretability
ECG	DL (AI- cirrhosis-ECG score)	Standard clinical evaluation	Low-cost cirrhosis screening with high AUC (0.908); potential for routine, scalable screening	Low-moderate (retrospective, single-center), emerging	Limited sample size; lack of external validation; implementation barriers despite low test cost

RF: Random forest; ANN: Artificial neural networks; CT: Computerized tomography; MRI: Magnetic resonance imaging; DL: Deep learning; CNN: Convolutional neural networks; AUC: Area under the curve; AI: Artificial intelligence; LightGBM: Light Gradient Boosting Machine; ECG: Electrocardiography; MASLD: Metabolic dysfunction-associated steatotic liver disease; FIB-4: Fibrosis-4; FIB-6: Fibrosis-6.