Evaluating laboratory-based AI across different liver disease spectra: reciprocal cross-cohort validation and comparison with FIB-4

Background Artificial intelligence (AI) may improve non-invasive liver fibrosis assessment, but its performance across different liver disease spectra remains uncertain. We evaluated laboratory-based AI models across two clinically distinct cohorts from a single institution and quantified their incremental performance over FIB-4. Methods Two independent cohorts were analyzed: MASLD ( n = 135) and heterogeneous chronic liver disease (HEPATO; n = 126). Models using six routine laboratory biomarkers were evaluated through reciprocal cross-cohort validation and pooled three-fold cross-validation. Discrimination was compared with FIB-4 using paired DeLong tests, and classifications at approximately 80% and 90% sensitivity using McNemar tests. Six eligible literature studies informed a random-effects meta-analysis, Bayesian evidence synthesis, and clinical operating-point simulations. Results Naive Bayes achieved AUCs of 0.76 in HEPATO and 0.75 in MASLD, compared with 0.68 and 0.72 for FIB-4, respectively; AUC differences were not statistically significant. In reciprocal cross-cohort operating-point analyses, AI showed numerically higher specificity than FIB-4 across all four validation scenarios, although achieved sensitivities differed between models, limiting direct specificity comparisons. Exploratory in-sample analyses suggested potential specificity gains requiring independent validation. The literature-only pooled Δ AUC was +0.074 (95% CI, +0.008 to +0.141; I 2 = 82.3%). Bayesian updating yielded a posterior mean Δ AUC of +0.063 (95% CrI, approximately +0.0001 to +0.125), with an approximately 84.7% probability of Δ AUC >0.03. Simulated specificity gains were +10.9 and +10.2 percentage points at 80% and 90% sensitivity, respectively. Conclusions Laboratory-based AI models demonstrated relatively consistent discrimination across two clinically distinct liver disease cohorts within a single-center setting. AI showed numerically higher specificity than FIB-4 across reciprocal cross-cohort operating-point analyses, although differences in achieved sensitivity preclude conclusions of superiority at matched operating points.