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

Table 1 Artificial intelligence-based studies on drug-induced liver injury prediction, highlighting their methodological frameworks and key performance outcomes

Ref.	Methodology	Results
Mostafa et al[61], 2024	RF & MLP on large human DILI datasets, externally validated on failed drug candidates	RF accuracy 63%, MLP MCC 0.245; models flagged failed drugs in external test set
Lesiński et al[62], 2021	RF combining gene expression & molecular descriptors	AUC approximately 0.73 (high vs low-risk classification)
Liu et al[63], 2022	Gene-expression cascade modeling preceding DILI histopathology	Mechanistic insights into pathways & TFs
Wang et al[64], 2022	ML on microarray data	AUC > 0.80 for genes DDIT3, GADD45A, SLC3A2, RBM24
Rao et al[65], 2023	SVM, RF, ANN on physicochemical & offtarget features for small molecules	AUC 0.88; sensitivity 0.73; specificity 0.90
Li et al[66], 2021	DeepDILI: Deep learning combining coupled ML + Mold2 descriptors	MCC 0.331; outperformed conventional ML (RF, SVM)
Li et al[67], 2020	8-layer deep neural network on human cell-line transcriptomics (L1000)	Training/IV AUC 0.802/0.798; balanced accuracies approximately 0.74
Xiao et al[68], 2024	XGBoost, RF, LASSO for TB treatment DILI prediction with SHAP interpretability	AUROC 0.89 in validation; strong model interpretability
Lee and Yoo[69], 2024	InterDILI interpretable RF model on multi-dataset integration (substructures, descriptors)	AUROC 0.88-0.97; AUPRC 0.81-0.95; feature insights

RF: Random forest; MLP: Multi-Layer Perceptron; DILI: Drug-induced liver injury; MCC: Matthew’s correlation coefficient; AUC: Area under the curve; TFs: Transcription factors; ML: Machine learning; SVM: Support vector machines; ANN: Artificial neural network; XGBoost: Extreme gradient boosting; LASSO: Least absolute shrinkage and selection operator; TB: Tuberculosis; SHAP: SHapley Additive exPlanations; AUROC: Area under the receiver operating characteristic curve; AUPRC: Area under precision recall curve.

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.