Machine learning

Machine learning (ML) is a field of artificial intelligence concerned with computational methods that identify patterns in data and use them to generate predictions, classifications, or decisions. In contrast to rule-based systems, ML models estimate relationships from examples, such as clinical variables, laboratory measurements, medical images, text, or molecular profiles. Common approaches include logistic regression, support vector machine, random forest, XGBoost, and LightGBM; deep learning represents a related family of models that uses multilayer neural networks. Model performance may be assessed with measures such as accuracy, sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve.

In biomedicine, ML is used for risk stratification, diagnosis, prognosis, image segmentation, clinical decision support, and analysis of high-dimensional biological data. Its potential value lies in integrating multiple predictors—including static patient characteristics and changing biomarkers—to estimate individualized risks. Interpretability methods such as SHapley Additive exPlanations and XGBoost-SHAP can help describe how input variables contribute to a prediction, while decision curve analysis can assess the potential clinical usefulness of a model. ML is therefore a computational tool rather than a biological treatment or pharmacological mechanism; its clinical significance depends on data quality, validation, interpretability, and successful integration into care.