Toward Clinically Actionable Explainable AI in Pulmonary Arterial Hypertension: Endpoints, Calibration, and External Validation. Comment on Ledziński et al. Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence f

We read with great interest the recent contribution proposing a machine-learning model (XGBoost), developed using registry data, to estimate mortality risk in adult patients with pulmonary arterial hypertension (PAH) and incorporating an SHAP-based interpretability strategy to clarify, both globally and at the individual level, the determinants of the prediction [...].