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Artificial Intelligence in Cardiology.

Kipp W Johnson1, Jessica Torres Soto2, Benjamin S Glicksberg3

  • 1Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.

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Artificial intelligence (AI) and machine learning (ML) are transforming cardiology. This guide helps clinicians understand AI/ML applications for precision cardiology and improved patient outcomes.

Keywords:
artificial intelligencecardiologymachine learningprecision medicine

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly impacting healthcare.
  • Cardiology is a field ripe for AI/ML integration due to complex data and predictive needs.

Purpose of the Study:

  • To guide clinicians on AI/ML concepts relevant to cardiology.
  • To review current AI/ML applications in cardiovascular medicine.
  • To identify future integration strategies for AI/ML in cardiology.

Main Methods:

  • Review of predictive modeling concepts (e.g., feature selection, dichotomization).
  • Discussion of supervised learning algorithms and their cardiovascular applications.
  • Explanation of deep learning and unsupervised learning methods with examples.

Main Results:

  • AI/ML offers significant potential for predictive modeling in cardiology.
  • Supervised and unsupervised learning methods have diverse applications in cardiovascular medicine.
  • Deep learning enables advanced pattern recognition for personalized patient care.

Conclusions:

  • AI/ML integration can enhance diagnostic accuracy and treatment strategies in cardiology.
  • Precision cardiology, driven by AI/ML, promises improved patient outcomes.
  • Clinicians need to understand AI/ML to leverage its full potential in cardiovascular medicine.