Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: A short narrative review of diagnostic and therapeutic implications

Table 2 Artificial intelligence-assisted prognostication and fibrosis-risk assessment in Metabolic dysfunction-associated steatotic liver disease

Ref.	Population/data	Model/output	Main quantitative finding	Focused limitation
Van Vleck et al[14]	EHR data from 38575 participants; 2281 patients were identified with historically defined NAFLD, including 619 documented only in radiology reports	NLP combining structured and unstructured EHR data to identify disease and examine progression to NASH or cirrhosis	NLP achieved a sensitivity of 0.93 and an F2 score of 0.92. Among the 619 radiology-only cases, 170 patients (28.4%) were later considered likely to have developed NASH or cirrhosis after a median of 1057.3 days	Retrospective and documentation-dependent; historical terminology; progression was inferred from EHR documentation rather than prospectively adjudicated outcomes
Sherman et al[17]	More than 1200 pathology reports used for manual validation; prognostic cohort of 3134 patients with biopsy-confirmed MASLD followed for 20604 person-years	NLP extraction of steatosis, lobular inflammation, ballooning, and fibrosis stage from free-text pathology reports	Positive and negative predictive values ranged from 93.5% to 100%. Compared with simple steatosis, adjusted hazard ratios for cirrhosis were 1.04 for MASH/F0, 1.19 for MASH/F1, 1.89 for MASH/F2, and 4.21 for MASH/F3	Retrospective study within one multihospital health system; transportability across institutions and pathology-reporting practices requires evaluation
Njei et al[37]	Development cohort of 1487 NHANES 2017-2020 participants; external biopsy cohort of 337 patients; mortality cohort of 4276 NHANES III participants	Explainable XGBoost FibroX model for advanced fibrosis and long-term mortality prediction, interpreted using SHAP	FibroX achieved an AUROC of 0.97 vs 0.62 for FIB-4 in development and 0.84 vs 0.82 in the biopsy cohort. The mortality C-statistic was 0.80; the adjusted hazard ratio for cardiovascular mortality was 1.22	The development reference standard combined non-invasive assessment with transient elastography rather than biopsy; prospective clinical-impact and decision-threshold evaluation remain necessary
Njei et al[38]	Systematic review of 26 studies published during 2014-2025 across Europe, the United States, Asia, and South America	AI-based prediction of MASH and fibrosis stages ≥ F2-F4 using clinical, laboratory, imaging, and elastography variables	Validation/test AUROCs were approximately 0.76-0.95 for MASH, with external-validation performance as low as 0.67, and approximately 0.72-0.94 for fibrosis ≥ F2-F4	Considerable heterogeneity in populations, predictors, outcomes, and study designs; data diversity and model interpretability remain important barriers to clinical translation

AI: Artificial intelligence; AUROC: Area under the receiver operating characteristic curve; EHR: Electronic health record; FIB-4: Fibrosis-4 Index; MASH: Metabolic dysfunction-associated steatohepatitis; MASLD: Metabolic dysfunction-associated steatotic liver disease; NAFLD: Non-alcoholic fatty liver disease; NASH: Non-alcoholic steatohepatitis; NHANES: National Health and Nutrition Examination Survey; NLP: Natural language processing; SHAP: SHapley Additive exPlanations; XGBoost: Extreme gradient boosting.