A multicenter study evaluated an explainable machine learning model intended to predict which atrial fibrillation patients with heart failure are more likely to benefit from AF ablation. The model combined features from cardiac CT angiography with clinical data and produced individualized outcome estimates for response after the procedure. If validated further, the work could shift ablation selection from largely observational predictors to a more standardized risk-benefit model, potentially reducing non-responders and improving resource allocation. The explainability focus also suggests the developers want the predictions to be clinically interpretable rather than purely predictive, aiming for adoption by cardiology workflows that require transparent decision support.