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.

Table 2 Key challenges and minimum requirements before clinical use of artificial intelligence

Challenge	Why performance may be misleading	Minimum requirement
Small effective sample and few events	Unstable estimates, wide uncertainty, and overfitting especially when n < 100	Justified sample/event count; confidence intervals; multicenter collaboration
Internal validation only	Random splits retain site, scanner, and practice signatures	Temporal and site-level external validation in the intended population
Information leakage	Patient, slide, or future information appears in both training and test data	Patient-level separation; locked preprocessing; independent test handling
Weak comparator	A high AUROC may not improve on an accepted score or specialist assessment	Head-to-head comparison with current care and incremental-value analysis
Discrimination without calibration	Rank ordering can be good while absolute risk is wrong	Calibration plot/slope, Brier score, and decision-curve benefit
Unrepresentative cohort	Performance and harm may differ by ancestry, sex, age, disease stage, or site	Prespecified subgroup and fairness analyses; representative recruitment
No workflow or outcome study	Offline accuracy does not establish better decisions or outcomes	Silent prospective testing followed by monitored implementation trial
Model and data drift	Assays, scanners, populations, and practice change after deployment	Performance monitoring, human override, recalibration, and retirement plan

AUROC: Area under the receiver operating characteristic curve.