Machine learning in cardiology: Clinical application and basic research

Jin Komuro, Dai Kusumoto, Hisayuki Hashimoto, Shinsuke Yuasa

Research output: Contribution to journalReview articlepeer-review


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 medical 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 increasingly 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 analysis. Machine learning is essential for genome and multi-omics analyses. This review summarizes the recent advancements in the use of machine learning in clinical applications and basic cardiovascular research.

Original languageEnglish
Pages (from-to)128-133
Number of pages6
JournalJournal of Cardiology
Issue number2
Publication statusPublished - 2023 Aug


  • Basic research
  • Clinical application
  • Machine learning

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine


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