Successes and challenges of artificial intelligence in cardiology

In the past decades there has been a substantial evolution in data management and data processing techniques. New data architectures made analysis of big data feasible, healthcare is orienting towards personalized medicine with digital health initiatives, and artificial intelligence (AI) is becoming...

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Autores principales: Vandenberk, Bert, Chew, Derek S., Prasana, Dinesh, Gupta, Sunny, Exner, Derek V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Digital Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336354/
https://www.ncbi.nlm.nih.gov/pubmed/37448836
http://dx.doi.org/10.3389/fdgth.2023.1201392
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author Vandenberk, Bert
Chew, Derek S.
Prasana, Dinesh
Gupta, Sunny
Exner, Derek V.
author_facet Vandenberk, Bert
Chew, Derek S.
Prasana, Dinesh
Gupta, Sunny
Exner, Derek V.
author_sort Vandenberk, Bert
collection PubMed
description In the past decades there has been a substantial evolution in data management and data processing techniques. New data architectures made analysis of big data feasible, healthcare is orienting towards personalized medicine with digital health initiatives, and artificial intelligence (AI) is becoming of increasing importance. Despite being a trendy research topic, only very few applications reach the stage where they are implemented in clinical practice. This review provides an overview of current methodologies and identifies clinical and organizational challenges for AI in healthcare.
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spelling pubmed-103363542023-07-13 Successes and challenges of artificial intelligence in cardiology Vandenberk, Bert Chew, Derek S. Prasana, Dinesh Gupta, Sunny Exner, Derek V. Front Digit Health Digital Health In the past decades there has been a substantial evolution in data management and data processing techniques. New data architectures made analysis of big data feasible, healthcare is orienting towards personalized medicine with digital health initiatives, and artificial intelligence (AI) is becoming of increasing importance. Despite being a trendy research topic, only very few applications reach the stage where they are implemented in clinical practice. This review provides an overview of current methodologies and identifies clinical and organizational challenges for AI in healthcare. Frontiers Media S.A. 2023-06-28 /pmc/articles/PMC10336354/ /pubmed/37448836 http://dx.doi.org/10.3389/fdgth.2023.1201392 Text en © 2023 Vandenberk, Chew, Prasana, Gupta and Exner. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Digital Health
Vandenberk, Bert
Chew, Derek S.
Prasana, Dinesh
Gupta, Sunny
Exner, Derek V.
Successes and challenges of artificial intelligence in cardiology
title Successes and challenges of artificial intelligence in cardiology
title_full Successes and challenges of artificial intelligence in cardiology
title_fullStr Successes and challenges of artificial intelligence in cardiology
title_full_unstemmed Successes and challenges of artificial intelligence in cardiology
title_short Successes and challenges of artificial intelligence in cardiology
title_sort successes and challenges of artificial intelligence in cardiology
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336354/
https://www.ncbi.nlm.nih.gov/pubmed/37448836
http://dx.doi.org/10.3389/fdgth.2023.1201392
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