Abstract
Machine learning has become popular in clinical practice, and the amount of research that uses artificial intelligence is rapidly increasing. In contrast to conventional statistical and rule-based methods, machine learning creates algorithms based only on combinations of input and output databases. Basic understanding of the internal workings of artificial intelligence, its structures and need for appropriate databases, as well as its strengths and weaknesses is important for efficient machine learning application. The cardiological applications of machine learning include diagnosing coronary artery diseases and heart failure, and examples are addressed herein. A preliminary application of machine learning to a 123I-metaiodobenzylguanidine-based risk model appears promising, and further studies using similar approaches are anticipated. Nuclear medicine physicians and cardiologists should play key roles in developing machine learning-based methods to ensure practical and reliable decisions.
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