Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature

Core Tip

Core Tip: Machine-learning models for detecting advanced fibrosis in pediatric metabolic dysfunction-associated steatotic liver disease routinely report striking accuracy, yet three recurring flaws limit clinical impact: Overfitting to single-center datasets, absence of multi-ethnic external validation, and dependence on costly, non-routine biomarkers. We outline a pragmatic roadmap: Prospective, multi-site cohorts with standardized liver histology, decision-curve and cost-utility analyses, and transparent model explainability to transform promising algorithms into trustworthy tools. Until these steps are fulfilled, the most reliable strategy combines simple serum tests (alanine aminotransferase, platelet-based indices), vibration-controlled transient elastography, and judicious, targeted liver biopsy for indeterminate or high-risk cases.