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AI and the cardiologist: when mind, heart and machine unite

Artificial intelligence (AI) and deep learning has made much headway in the consumer and advertising sector, not only affecting how and what people purchase these days, but also affecting behaviour and cultural attitudes. It is poised to influence nearly every aspect of our being, and the field of c...

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Detalles Bibliográficos
Autores principales: D'Costa, Antonio, Zatale, Aishwarya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8705226/
https://www.ncbi.nlm.nih.gov/pubmed/34949649
http://dx.doi.org/10.1136/openhrt-2021-001874
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author D'Costa, Antonio
Zatale, Aishwarya
author_facet D'Costa, Antonio
Zatale, Aishwarya
author_sort D'Costa, Antonio
collection PubMed
description Artificial intelligence (AI) and deep learning has made much headway in the consumer and advertising sector, not only affecting how and what people purchase these days, but also affecting behaviour and cultural attitudes. It is poised to influence nearly every aspect of our being, and the field of cardiology is not an exception. This paper aims to brief the clinician on the advances in AI and machine learning in the field of cardiology, its applications, while also recognising the potential for future development in these two mammoth fields. With the advent of big data, new opportunities are emerging to build AI tools, with better accuracy, that will directly aid not only the clinician but also allow nations to provide better healthcare to its citizens.
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spelling pubmed-87052262022-01-10 AI and the cardiologist: when mind, heart and machine unite D'Costa, Antonio Zatale, Aishwarya Open Heart Basic and Translational Research Artificial intelligence (AI) and deep learning has made much headway in the consumer and advertising sector, not only affecting how and what people purchase these days, but also affecting behaviour and cultural attitudes. It is poised to influence nearly every aspect of our being, and the field of cardiology is not an exception. This paper aims to brief the clinician on the advances in AI and machine learning in the field of cardiology, its applications, while also recognising the potential for future development in these two mammoth fields. With the advent of big data, new opportunities are emerging to build AI tools, with better accuracy, that will directly aid not only the clinician but also allow nations to provide better healthcare to its citizens. BMJ Publishing Group 2021-12-23 /pmc/articles/PMC8705226/ /pubmed/34949649 http://dx.doi.org/10.1136/openhrt-2021-001874 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Basic and Translational Research
D'Costa, Antonio
Zatale, Aishwarya
AI and the cardiologist: when mind, heart and machine unite
title AI and the cardiologist: when mind, heart and machine unite
title_full AI and the cardiologist: when mind, heart and machine unite
title_fullStr AI and the cardiologist: when mind, heart and machine unite
title_full_unstemmed AI and the cardiologist: when mind, heart and machine unite
title_short AI and the cardiologist: when mind, heart and machine unite
title_sort ai and the cardiologist: when mind, heart and machine unite
topic Basic and Translational Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8705226/
https://www.ncbi.nlm.nih.gov/pubmed/34949649
http://dx.doi.org/10.1136/openhrt-2021-001874
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