Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models

Table 2 Artificial intelligence-augmented vs human-only diagnostic accuracy: Current evidence

Task	AI model/dataset	AI performance	Comparator	Outcome
CT-based HCC detection[6]	CNN on CT (deep segmentation, auto segment)	Sensitivity approximately 92%, specificity approximately 97%	Radiologists	Outperformed (AI Sn/Sp 92/98 vs 82.5/96.5); supports workflow
PLAN-B-DF (internal/external validation)[70]	Auto segmentation + clinical data	C-index 0.91; 0.89	Traditional risk scores	Outperformed
Ultrasound focal lesion detection[81]	DL on B-mode US	AUC approximately 0.93	Sonographers	Comparable performance
Radiomics MVI in HCC[82]	Deep learning (large meta analysis)	AUC approximately 0.97	Non-DL ML (AUC 0.82)	DL superior
Histopathology slide review[38]	DL assistance	Accuracy approximately 0.885	Pathologists	Assisted improvements but risks of misguidance noted

AI: Artificial intelligence; CT: Computed tomography; HCC: Hepatocellular carcinoma; CNN: Convolutional neural network; DL: Deep learning; US: Ultrasonography; AUC: Area under the curve; MVI: Microvascular invasion; ML: Machine learning.