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

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.