Machine learning in cardiology: Clinical application and basic research

Machine learning is a subfield of artificial intelligence. The quality and versatility of machine learning have been rapidly improving and playing a critical role in many aspects of social life. This trend is also observed in the med-ical field. Generally, there are three main types of machine learning: supervised, unsupervised, and reinforcement learning. Each type of learning is adequately selected for the purpose and type of data. In the field of medicine, various types of information are collected and used, and research using machine learning is becoming increas-ingly relevant. Many clinical studies are conducted using electronic health and medical records, including in the cardiovascular area. Machine learning has also been applied in basic research. Machine learning has been widely used for several types of data analysis, such as clustering of microarray analysis and RNA sequence anal-ysis. Machine learning is essential for genome and multi-omics analyses. This review summarizes the recent ad-vancements in the use of machine learning in clinical applications and basic cardiovascular research.& COPY; 2023 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Automated summary

Machine learning, as a subfield of artificial intelligence, is rapidly improving in quality and versatility, playing a critical role in various aspects of social life. In the medical field, it is increasingly used for research and applied in clinical and basic cardiovascular studies.