Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.
Bibliographical noteFunding Information:
Conflict of interest: J.K.M. serves on the scientific advisory board of Arineta Ltd., on the speaker’s bureau of GE Healthcare, and owns equity in Cleerly Inc. U.J.S. receives institutional research support from Astellas Pharma Inc., Bayer Healthcare, GE Healthcare, Siemens and has received compensation for consulting and/or speaking from Bayer Healthcare, GE Healthcare, Guerbet LLC, HeartFlow Inc., and Siemens. J.L. is a consultant and has stock options in Circle CVI and Heartflow Inc, institutional corelab agreements with Edwards Lifesciences, Abbott, Medtronic and institutional research grant from GE Healthcare. K.N. has received unrestricted institutional research support from Siemens Healthineers, GE Healthcare, Bayer Healthcare and Heartflow Inc. D.S.B. has software royalties from Cedars-Sinai Medical Center. All other authors declared no conflict of interest.
All Science Journal Classification (ASJC) codes
- Cardiology and Cardiovascular Medicine