Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches

Table 2 Studies on artificial intelligence for predicting metabolic dysfunction-associated steatotic liver disease, steatohepatitis, and fibrosis based on clinical data

Ref.	Sample size	Machine learning type	Comparator	Reference standard	Classification categories	Model performance	Additional information
Qin et al[36], 2023	n = 14439 general population	SVM; RF	None	Color Doppler ultrasound (3.5-MHz, expert-interpreted)	MASLD diagnosis	AUC: SVM 0.85, RF 0.852; Acc: SVM 0.81, RF 0.78
Dabbah et al[37], 2025	Training: n = 618 MASLD; Validation: n = 540	XGBoost	FIB-4; NFS	Elastography ≥ 9.3 kPa/Biopsy ≥ F3	Advanced fibrosis	AUC 0.91; Sen 91%; Spe 76%	AUC; FIB-4 0.78; NFS 0.81
Nabrdalik et al[38], 2024	n = 2000 with DMT2	MLR	None	Ultrasonography plus metabolic criteria	MASLD diagnosis	AUC 0.84; Sen 75%; Spe 79%	Unsupervised ML was applied to identify a cluster of patients at high risk for MASLD
Njei et al[39], 2024	n = 5281	XGBoost	FIB-4; APRI; NFS; BARD	FibroScan-AST score (≥ 0.35/≥ 0.67)	High-risk MASH	AUC 0.95; Sen 82%; Spe 91%	AUC: FIB-4 0.50; NFS 0.54; BARD 0.39; APRI 0.50
Yang et al[40], 2024	n = 14913	LGBM; XGboost; RF	None	Transient elastography (CAP, LSM)	MASLD diagnosis	AUC; LGBM 0.90; XGboost 0.89; RF 0.89	The SHAP method was applied to enhance model interpretability
Boullion et al[41], 2025	n = 15560	RF	None	Transient elastography CAP ≥ 238 dB/m (steatosis)/LSM ≥ 7 kPa (fibrosis)	MASLD diagnosis Fibrosis	Acc; Steatosis: 79.5%; Fibrosis: 86.07%
Wakabayashi et al[42], 2025	n = 463	SVM; XGBoost; LR	FIB-4; APRI	Liver biopsy	Significant fibrosis (≥ F2)	AUC; SVM 0.88; LR 0.87; XGB 0.85	AUC: FIB-4 0.88; APRI 0.85
Xiong et al[43], 2025	Training n = 522; Validation n = 224	XGBoost	APRI; FIB-4	Liver biopsy	Advanced fibrosis	AUC 0.917	AUC; APRI 0.73; FIB-4 0.752
Zhu et al[44], 2025	n = 10007	LR; XGBoost	None	Transient elastography (CAP)	MASLD diagnosis	AUC; LR 0.79; XGBoost 0.79	The NHANES dataset was used as an external validation cohort

Acc: Accuracy; APRI: Aspartate aminotransferase-to-platelet ratio index; AST: Aspartate aminotransferase; AUC: Area under the curve; BARD: Body mass index, aspartate aminotransferase/alanine transaminase ratio, and diabetes score; CAP: Controlled attenuation parameter; DMT2: Type 2 diabetes mellitus; EMR: Electronic medical record; FIB-4: Fibrosis-4 index; LGBM: Light gradient boosting machine; LR: Logistic regression; LSM: Liver stiffness measurement; MASH: Metabolic dysfunction-associated steatohepatitis; MASLD: Metabolic dysfunction-associated steatotic liver disease; ML: Machine learning; MLR: Multivariate logistic regression; NFS: Nonalcoholic fatty liver disease fibrosis score; NHANES: National Health and Nutrition Examination Survey; RF: Random Forest; Sen: Sensitivity; SHAP: SHapley Additive exPlanations; Spe: Specificity; SVM: Support vector machine; XGBoost: Extreme gradient boosting.