Artificial intelligence in the diagnosis and management of arrhythmias

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care. As artificial intelligence (AI) has entered the medical field in recent years, machine learning (ML) approaches have made progress in assisting healthcare professionals in optimizing personalized treatment in a given situation, in particular in electrocardiography and image interpretation. Artificial intelligence methodologies are increasingly being adopted into all aspects of patient care and are paving the way to minimally invasive or non-invasive treatment modalities. This article offers a state-of-the-art overview on milestones achieved, but also on future integration of this information into diagnostic and therapeutic measures, and its likely impact on all aspects of arrhythmia care.