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

Table 1 Types of artificial intelligence, application models, and their utility in medicine

Type of artificial intelligence		Basic functioning	Medical utility
ML		Algorithms that learn patterns from structured data	Predictive diagnosis, risk analysis, disease classification, support for clinical decision-making, and selection of relevant variables in large clinical datasets
Classical ML methods	RF	Learning algorithm based on the construction of multiple DT, incorporating randomization to improve accuracy
	GBM	Technique that sequentially trains multiple DT, correcting the errors of the previous tree using gradients
	XGBoost	Optimized version of GBM that incorporates regularization, tree pruning, and parallel processing to enhance speed and performance
	SVM	Algorithm that identifies the optimal hyperplane that separates classes by maximizing the margin
	Logistic regression	Linear statistical model that estimates the probability of a binary event using the logistic function
DL		A subtype of ML that uses deep neural networks to process large volumes of data in order to identify patterns	Interpretation of unstructured data, including medical images (radiology and histopathology), omics and genomic data, and clinical text. It also facilitates information extraction from medical records and supports predictive analytics for clinical outcomes
DL subtypes	CNN	Uses convolutions to detect spatial patterns in structured data. Comprised of convolutional and pooling layers
	Transformer	Model based on attention mechanisms that enables parallel processing of entire sequences, capturing complex relationships among words or data
	MLP	Feedforward neural network with one or more hidden layers. Each neuron applies a nonlinear activation function to learn complex representations
NLP		Algorithms that comprehend and process human language in clinical texts	Extraction of information from electronic health records, analysis of medical notes, medical chatbots
Unsupervised learning		Algorithms that identify patterns or groupings in unlabeled data, capable of detecting subclusters, outliers, or low-dimensional data representations	Detection of disease subtypes, clustering of patients with similar clinical profiles
Reinforcement learning		Algorithms that learn through trial and error using feedback	Optimization of personalized treatments, sequential decision-making, such as drug dosing

Data adapted from Yu et al[23], Lidströmer et al[24], and Žigutytė et al[25]. ML: Machine learning; RF: Random Forest; DT: Decision trees; GBM: Gradient boosting machine; XGBoost: Extreme gradient boosting; SVM: Support vector machine; DL: Deep learning; CNN: Convolutional neural network; MLP: Multilayer perceptron; NLP: Natural language processing.

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.