Artificial intelligence in autoimmune liver diseases: Diagnosis, risk stratification, and personalized care

Table 1 Representative artificial intelligence studies across autoimmune liver diseases

Ref.	Clinical task	Model and input	Cohort and validation	Performance/conventional comparator	Clinical interpretation
Eaton et al[7]	PSC decompensation	PREsTo; gradient boosting, routine clinical data	509 derivations; 278 external validations	C-statistic 0.90 vs MELD 0.72, Mayo 0.85, ALP < 1.5 × ULN 0.65	Externally validated against clinical scores; no implementation trial
Zhang et al[19]	AIH significant fibrosis	Explainable XGBoost; clinical/Laboratory variables	261; multicenter retrospective validation	AUROC 0.791 vs APRI 0.557 and FIB-4 0.625	Moderate discrimination; prospective calibration and biopsy-impact testing needed
Gerussi et al[20]	PBC phenotypes	Unsupervised/supervised machine learning	11819 development; 1069 validation	Four reproducible phenotypes; not an AUC-comparator study	Phenotypes reproduced in a large validation cohort; treatment benefit remains untested
Gerussi et al[27]	PBC genetic risk	Logic learning machine; genetic variants	1345 training; 834 validation	Validation AUROC 0.73; sensitivity 0.55, specificity 0.74	Transparent rules; screening utility unproven
Hu et al[31]	PSC cholangiocarcinoma risk	Cox regression, random survival forest, and gradient boosting	1459 single center; bile-acid subset 300	Best C-indices across assessment times 0.68-0.71; combined subset 0.67; better than common PSC scores	Modest discrimination; no prospective surveillance benefit
Wang et al[33]	DILI vs AIH	BJ-AID gradient boosting; five laboratory variables	2554 across 10 hospitals; external and prospective cohorts	AUROC 0.94 development, 0.91 external, 0.93 prospective; atypical 0.85	Adjunct only; geographic concentration and no management-impact trial
Gerussi et al[37]	AIH vs PBC histology	ALNE deep learning; whole-slide histology	354 patients; 92 external validation	External AUROC 0.81; general-pathologist κ 0.09	Externally tested adjunct; binary task and small external set
Allaume et al[38]	AIH histology	Weakly supervised deep learning; whole slides	402 training; 185 external	AUROC 0.92 internal, 0.74 external; HBV 0.42, DILI 0.76	Marked external degradation; transportability remains limited
Ercan et al[39]	AIH histological features	AI(H); manually annotated biopsy features	123 biopsies; internal assessment	Accuracy 72.4%-99.4% across features	Annotation-intensive feasibility study, not externally validated diagnosis
Wei et al[42]	AIH advanced fibrosis	Random forest; routine variables	233 discovery; 33 validation	AUROC 0.951 training, 0.863 test, 0.843 validation	Small validation set and probable optimism
Kimura et al[54]	PBC UDCA response	XGBoost; baseline/early clinical variables	245 from 18 hospitals; 51 tests	AUROC 0.811 development, 0.856 test; Paris II outcome	Small test set; model-guided treatment not evaluated
Yang et al[55]	PBC UDCA response	Metabolomic machine learning	347 non-cirrhotic patients	AUROC 0.91 response, 0.87 deep response; better than clinical-only	Selected population and platform dependence; no treatment trial
Fu et al[61]	PBC survival	Random survival forest	90; single center, internal validation	C-index 0.7805; 1-/3-/5-year AUROC 0.9595/0.8898/0.9088	High estimates in a small cohort require external replication
Vuppalanchi et al[73]	PSC outcomes	M + BA quantitative MRCP metric	102 derivations; 50 validations	AUROC 0.86; hazard ratio 5.8; better than Mayo score	Quantitative biomarker; limited validation size
Ragab et al[66]	PSC diagnosis	DeePSC; multi-view convolutional neural network on MRCP	342 PSC + 264 controls; small internal tests; 37 external	Accuracy 80.5% (3 T), 82.6% (1.5 T), 92.4% external; radiologist differences not significant	Case-control task, scanner effects, and small test cohorts
Singh et al[74]	PSC decompensation	3-dimensional dense convolutional network; computed tomography	277; 128 events; five-fold cross-validation	AUROC 0.89 ± 0.04; no external comparator cohort	Internal-only estimate; center-specific overfit possible
Snir et al[77]	PSC proteomics	Elastic net; 2870 serum proteins	45 PSC + 30 controls; discovery study	Top markers AUROC > 0.90 for disease and > 0.80 for ELF-defined disease severity	High-dimensional small-sample discovery; requires independent replication
Colapietro et al[92]	AIH counseling	GPT-4; 12 questions from four patients	11 expert raters; no patient outcomes	Median accuracy 5/6, completeness 2/3, safety 3/3; poor/slight agreement	Interface study; version dependence and subjective evaluation

AIH: Autoimmune hepatitis; PBC: Primary biliary cholangitis; PSC: Primary sclerosing cholangitis; DILI: Drug-induced liver injury; UDCA: Ursodeoxycholic acid; PREsTo: Primary sclerosing cholangitis risk estimate tool; XGBoost: EXtreme Gradient Boosting; BJ-AID: Beijing-AID model for discriminating drug-induced liver injury from autoimmune hepatitis; ALNE: Autoimmune liver neural estimator; M + BA: Composite risk score combining quantitative magnetic resonance cholangiopancreatography metrics with total bilirubin and aspartate aminotransferase; MRCP: Magnetic resonance cholangiopancreatography; DeePSC: Deep learning model for primary sclerosing cholangitis; GPT-4: Generative pre-trained transformer 4; ALP: Alkaline phosphatase; ULN: Upper limit of normal; MELD: Model for end-stage liver disease; APRI: Aspartate aminotransferase-to-platelet ratio index; AUROC: Area under the receiver operating characteristic curve; FIB-4: Fibrosis-4 index; HBV: Hepatitis B virus; ELF: Enhanced liver fibrosis; AUC: Area under the curve.