Explainable artificial intelligence approaches in cardiovascular imaging: methodological advances and clinical implications

Cardiovascular diseases remain the leading cause of mortality worldwide, making accurate and efficient imaging-based diagnosis indispensable. Modern modalities such as Coronary Computed Tomography Angiography, Cardiac Magnetic Resonance, Echocardiography, and Chest X-Ray enable rich structural and functional assessment; however, the rapid growth of imaging data strains traditional analysis. Deep learning has markedly improved performance across cardiovascular imaging tasks, yet its “black box” nature limits interpretability, clinician trust, and clinical adoption. eXplainable Artificial Intelligence (XAI) addresses this gap by exposing the decision logic of models in human-understandable forms. This review provides a structured synthesis of recent progress in XAI for cardiovascular imaging. We outline the core principles of perturbation-based and backpropagation-based methods, and survey their applications across major modalities for disease characterization, lesion discrimination, and risk stratification. We further analyze current evaluation challenges and methodological limitations, and propose future directions toward robust, trustworthy, and clinically deployable XAI systems.