Artificial intelligence in preventive cardiology.
Journal: Progress in cardiovascular diseases
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Affiliated Institutions:
Faculty of Health Sciences, Queen's University, Kingston, ON, Canada.
Cardiometabolics Unit, Mount Sinai Hospital, Mount Sinai Heart, NY, United States of America.
Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, United States of America.
Harrington Heart & Vascular Institute, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, United States of America.
Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States of America.
Section of Cardiology, The Aga Khan University, Texas Heart Institute, Baylor College of Medicine, Houston, TX, United States of America.
The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
John Ochsner Heart and Vascular Institute, Ochsner Clinical School, The University of Queensland School of Medicine, New Orleans, LA, USA.
Cardiology Division, NYU Langone Health and NYU School of Medicine, New York, NY, United States of America. Electronic address: Chayakrit.Krittanawong@gmail.com.
Abstract summary
Artificial intelligence (AI) is a field of study that strives to replicate aspects of human intelligence into machines. Preventive cardiology, a subspeciality of cardiovascular (CV) medicine, aims to target and mitigate known risk factors for CV disease (CVD). AI's integration into preventive cardiology may introduce novel treatment interventions and AI-centered clinician assistive tools to reduce the risk of CVD. AI's role in nutrition, weight loss, physical activity, sleep hygiene, blood pressure, dyslipidemia, smoking, alcohol, recreational drugs, and mental health has been investigated. AI has immense potential to be used for the screening, detection, and monitoring of the mentioned risk factors. However, the current literature must be supplemented with future clinical trials to evaluate the capabilities of AI interventions for preventive cardiology. This review discusses present examples, potentials, and limitations of AI's role for the primary and secondary prevention of CVD.
Authors & Co-authors:
El Sherbini
Rosenson
Al Rifai
Virk
Wang
Virani
Glicksberg
Lavie
Krittanawong
Study Outcome
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