Explainable Artificial Intelligence in Echocardiography

Recent advancements in artificial intelligence (AI) have generated novel opportunities and challenges in ultrasound imaging. Deep learning algorithms exhibit significant potential in analyzing echocardiographic images, encompassing tasks such as view classification, quantification of cardiac function, and the diagnosis and risk assessment of cardiac diseases. The “black box” nature of AI models limits their clinical applications. Adopting explainable artificial intelligence (XAI) methods is crucial for improving the transparency and understanding of model predictions. This paper reviews the progress of AI applications in echocardiography, with a particular emphasis on XAI as a technical solution to enhance the transparency of model decision-making and its benefits compared to traditional AI models. This review outlines recent advancements in XAI applications for echocardiography and their clinical implications.