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.