Artificial Intelligence in Precision Cardiovascular Medicine
Abstract Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of...
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| Veröffentlicht in: | Journal of the American College of Cardiology 2017-05, Vol.69 (21), p.2657-2664 |
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| container_title | Journal of the American College of Cardiology |
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| creator | Krittanawong, Chayakrit, MD Zhang, HongJu, PhD Wang, Zhen, PhD Aydar, Mehmet, PhD Kitai, Takeshi, MD, PhD |
| description | Abstract Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI’s application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine. |
| doi_str_mv | 10.1016/j.jacc.2017.03.571 |
| format | Article |
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AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI’s application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.</description><identifier>ISSN: 0735-1097</identifier><identifier>EISSN: 1558-3597</identifier><identifier>DOI: 10.1016/j.jacc.2017.03.571</identifier><identifier>PMID: 28545640</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Agents (artificial intelligence) ; Air pollution ; Algorithms ; Alzheimer's disease ; Analytics ; Anticoagulants ; Arrhythmia ; Arteriosclerosis ; Artificial Intelligence ; Attitude control ; Automation ; big data ; Bleeding ; Brain ; Cameras ; Cardiac arrhythmia ; Cardiology ; Cardiology - methods ; Cardiomyopathy ; Cardiovascular ; Cardiovascular disease ; Cardiovascular diseases ; Classification ; cognitive computing ; Computation ; Computer programs ; Coronary artery disease ; Data processing ; Deep learning ; Diabetes mellitus ; Diagnosis ; Failure analysis ; Fuzzy systems ; Gastrointestinal tract ; Genotypes ; Hazards ; Humans ; Hypertension ; Internal Medicine ; Intervention ; Learning algorithms ; Lungs ; Machine Learning ; Media ; Medical personnel ; Medicine ; Minority & ethnic groups ; Mortality ; Myocardial infarction ; Neural networks ; Neuroimaging ; Optimization ; pH effects ; Pharmacology ; Precision medicine ; Precision Medicine - methods ; Prevention ; Quantitation ; Risk factors ; Robots ; Silicon ; Social networks ; Speech ; Statins ; Statistics ; Surgery ; Survival ; Ventricle ; Visual perception</subject><ispartof>Journal of the American College of Cardiology, 2017-05, Vol.69 (21), p.2657-2664</ispartof><rights>American College of Cardiology Foundation</rights><rights>2017 American College of Cardiology Foundation</rights><rights>Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited May 30, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c439t-3bf6fbcc58fb6aaf9dc78469bfb089e49ee834ef0dac113dd55c0176b032b58f3</citedby><cites>FETCH-LOGICAL-c439t-3bf6fbcc58fb6aaf9dc78469bfb089e49ee834ef0dac113dd55c0176b032b58f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jacc.2017.03.571$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28545640$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Krittanawong, Chayakrit, MD</creatorcontrib><creatorcontrib>Zhang, HongJu, PhD</creatorcontrib><creatorcontrib>Wang, Zhen, PhD</creatorcontrib><creatorcontrib>Aydar, Mehmet, PhD</creatorcontrib><creatorcontrib>Kitai, Takeshi, MD, PhD</creatorcontrib><title>Artificial Intelligence in Precision Cardiovascular Medicine</title><title>Journal of the American College of Cardiology</title><addtitle>J Am Coll Cardiol</addtitle><description>Abstract Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI’s application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.</description><subject>Agents (artificial intelligence)</subject><subject>Air pollution</subject><subject>Algorithms</subject><subject>Alzheimer's disease</subject><subject>Analytics</subject><subject>Anticoagulants</subject><subject>Arrhythmia</subject><subject>Arteriosclerosis</subject><subject>Artificial Intelligence</subject><subject>Attitude control</subject><subject>Automation</subject><subject>big data</subject><subject>Bleeding</subject><subject>Brain</subject><subject>Cameras</subject><subject>Cardiac arrhythmia</subject><subject>Cardiology</subject><subject>Cardiology - methods</subject><subject>Cardiomyopathy</subject><subject>Cardiovascular</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Classification</subject><subject>cognitive computing</subject><subject>Computation</subject><subject>Computer programs</subject><subject>Coronary artery disease</subject><subject>Data processing</subject><subject>Deep learning</subject><subject>Diabetes mellitus</subject><subject>Diagnosis</subject><subject>Failure analysis</subject><subject>Fuzzy systems</subject><subject>Gastrointestinal tract</subject><subject>Genotypes</subject><subject>Hazards</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Internal Medicine</subject><subject>Intervention</subject><subject>Learning algorithms</subject><subject>Lungs</subject><subject>Machine Learning</subject><subject>Media</subject><subject>Medical personnel</subject><subject>Medicine</subject><subject>Minority & ethnic groups</subject><subject>Mortality</subject><subject>Myocardial infarction</subject><subject>Neural networks</subject><subject>Neuroimaging</subject><subject>Optimization</subject><subject>pH effects</subject><subject>Pharmacology</subject><subject>Precision medicine</subject><subject>Precision Medicine - methods</subject><subject>Prevention</subject><subject>Quantitation</subject><subject>Risk factors</subject><subject>Robots</subject><subject>Silicon</subject><subject>Social networks</subject><subject>Speech</subject><subject>Statins</subject><subject>Statistics</subject><subject>Surgery</subject><subject>Survival</subject><subject>Ventricle</subject><subject>Visual perception</subject><issn>0735-1097</issn><issn>1558-3597</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1u1TAQRi1ERS-FF2CBrsSGTcI4jp1YgkrVFT-VWoEErC1nPEYOuUmxk0p9mz4LT4ajW0DqoqvZnO_TzBnGXnAoOXD1pi97i1hWwJsSRCkb_ohtuJRtIaRuHrMNNEIWHHRzzJ6m1AOAarl-wo6rVtZS1bBh787iHHzAYIft-TjTMIQfNCJtw7j9EglDCtP4-3ZnowvTtU24DDZuL8nlyEjP2JG3Q6Lnd_OEff_w_tvuU3Hx-eP57uyiwFrouRCdV75DlK3vlLVeO2zaWunOd9BqqjVRK2ry4CxyLpyTEvNRqgNRdTkkTtjrQ-9VnH4tlGazDwnzsnakaUmGaxBcaV3pjL66h_bTEse83UpVtVaKV5mqDhTGKaVI3lzFsLfxxnAwq1zTm1WuWeUaECbLzaGXd9VLtyf3L_LXZgbeHgDKLq4DRZMwrDZdyCpn46bwcP_pvTgOYQxoh590Q-n_HSZVBszX9b3rd3kjVJs3EH8AqeKgHQ</recordid><startdate>20170530</startdate><enddate>20170530</enddate><creator>Krittanawong, Chayakrit, MD</creator><creator>Zhang, HongJu, PhD</creator><creator>Wang, Zhen, PhD</creator><creator>Aydar, Mehmet, PhD</creator><creator>Kitai, Takeshi, MD, PhD</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>7TK</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope></search><sort><creationdate>20170530</creationdate><title>Artificial Intelligence in Precision Cardiovascular Medicine</title><author>Krittanawong, Chayakrit, MD ; Zhang, HongJu, PhD ; Wang, Zhen, PhD ; Aydar, Mehmet, PhD ; Kitai, Takeshi, MD, PhD</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c439t-3bf6fbcc58fb6aaf9dc78469bfb089e49ee834ef0dac113dd55c0176b032b58f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Agents (artificial intelligence)</topic><topic>Air pollution</topic><topic>Algorithms</topic><topic>Alzheimer's disease</topic><topic>Analytics</topic><topic>Anticoagulants</topic><topic>Arrhythmia</topic><topic>Arteriosclerosis</topic><topic>Artificial Intelligence</topic><topic>Attitude control</topic><topic>Automation</topic><topic>big data</topic><topic>Bleeding</topic><topic>Brain</topic><topic>Cameras</topic><topic>Cardiac arrhythmia</topic><topic>Cardiology</topic><topic>Cardiology - methods</topic><topic>Cardiomyopathy</topic><topic>Cardiovascular</topic><topic>Cardiovascular disease</topic><topic>Cardiovascular diseases</topic><topic>Classification</topic><topic>cognitive computing</topic><topic>Computation</topic><topic>Computer programs</topic><topic>Coronary artery disease</topic><topic>Data processing</topic><topic>Deep learning</topic><topic>Diabetes mellitus</topic><topic>Diagnosis</topic><topic>Failure analysis</topic><topic>Fuzzy systems</topic><topic>Gastrointestinal tract</topic><topic>Genotypes</topic><topic>Hazards</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Internal Medicine</topic><topic>Intervention</topic><topic>Learning algorithms</topic><topic>Lungs</topic><topic>Machine Learning</topic><topic>Media</topic><topic>Medical personnel</topic><topic>Medicine</topic><topic>Minority & ethnic groups</topic><topic>Mortality</topic><topic>Myocardial infarction</topic><topic>Neural networks</topic><topic>Neuroimaging</topic><topic>Optimization</topic><topic>pH effects</topic><topic>Pharmacology</topic><topic>Precision medicine</topic><topic>Precision Medicine - methods</topic><topic>Prevention</topic><topic>Quantitation</topic><topic>Risk factors</topic><topic>Robots</topic><topic>Silicon</topic><topic>Social networks</topic><topic>Speech</topic><topic>Statins</topic><topic>Statistics</topic><topic>Surgery</topic><topic>Survival</topic><topic>Ventricle</topic><topic>Visual perception</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Krittanawong, Chayakrit, MD</creatorcontrib><creatorcontrib>Zhang, HongJu, PhD</creatorcontrib><creatorcontrib>Wang, Zhen, PhD</creatorcontrib><creatorcontrib>Aydar, Mehmet, PhD</creatorcontrib><creatorcontrib>Kitai, Takeshi, MD, PhD</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of the American College of Cardiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Krittanawong, Chayakrit, MD</au><au>Zhang, HongJu, PhD</au><au>Wang, Zhen, PhD</au><au>Aydar, Mehmet, PhD</au><au>Kitai, Takeshi, MD, PhD</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial Intelligence in Precision Cardiovascular Medicine</atitle><jtitle>Journal of the American College of Cardiology</jtitle><addtitle>J Am Coll Cardiol</addtitle><date>2017-05-30</date><risdate>2017</risdate><volume>69</volume><issue>21</issue><spage>2657</spage><epage>2664</epage><pages>2657-2664</pages><issn>0735-1097</issn><eissn>1558-3597</eissn><abstract>Abstract Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI’s application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>28545640</pmid><doi>10.1016/j.jacc.2017.03.571</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Agents (artificial intelligence) Air pollution Algorithms Alzheimer's disease Analytics Anticoagulants Arrhythmia Arteriosclerosis Artificial Intelligence Attitude control Automation big data Bleeding Brain Cameras Cardiac arrhythmia Cardiology Cardiology - methods Cardiomyopathy Cardiovascular Cardiovascular disease Cardiovascular diseases Classification cognitive computing Computation Computer programs Coronary artery disease Data processing Deep learning Diabetes mellitus Diagnosis Failure analysis Fuzzy systems Gastrointestinal tract Genotypes Hazards Humans Hypertension Internal Medicine Intervention Learning algorithms Lungs Machine Learning Media Medical personnel Medicine Minority & ethnic groups Mortality Myocardial infarction Neural networks Neuroimaging Optimization pH effects Pharmacology Precision medicine Precision Medicine - methods Prevention Quantitation Risk factors Robots Silicon Social networks Speech Statins Statistics Surgery Survival Ventricle Visual perception |
| title | Artificial Intelligence in Precision Cardiovascular Medicine |
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