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Abstract
The role of physicians has always been to synthesize the data available to them to
identify diagnostic patterns that guide treatment and follow response. Today, increasingly
sophisticated machine learning algorithms may grow to support clinical experts in
some of these tasks. Machine learning has the potential to benefit patients and cardiologists,
but only if clinicians take an active role in bringing these new algorithms into practice.
The aim of this review is to introduce clinicians who are not data science experts
to key concepts in machine learning that will allow them to better understand the
field and evaluate new literature and developments. The current published data in
machine learning for cardiovascular disease is then summarized, using both a bibliometric
survey, with code publicly available to enable similar analysis for any research topic
of interest, and select case studies. Finally, several ways that clinicians can and
must be involved in this emerging field are presented.