Cardiac diseases are the leading cause of death in several countries. They include not only chronic conditions such as arrhythmias and cardiomyopathies but also acute and serious diseases with cardiovascular events such as myocardial infarction. Considering the importance of cardiac function in maintaining life, early detection of cardiovascular diseases and appropriate diagnosis and treatment greatly affect the prognosis of patients. In many cases, surgical procedures play an important role in the treatment of cardiac diseases, including urgent cases and those that are non-responsive to medication. In addition to highly invasive cardiac surgeries, such as coronary artery bypass surgery and heart transplantation, which are often performed under open chest conditions, minimally invasive cardiac surgery has been expanding in recent years, with an increase in robotic surgeries and percutaneous interventions using catheters and other devices. Against this background, there is a growing demand for minimally invasive cardiac imaging that can provide preoperative information on the structure and function of the heart and any associated abnormalities. In the cardiovascular field, as in other medical fields, advances in imaging technology are improving evidence-based treatment decision-making and clinical outcomes. Simultaneously, there are growing expectations for artificial intelligence (AI) and machine learning (ML) approaches to properly and efficiently interpret the ever-increasing volume of clinical data. This chapter describes the major classifications of cardiac diseases and their treatment procedures. Next, each diagnostic modality for cardiac disease is outlined, with examples of recent applications of AI technology to these modalities.