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Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial...

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Autores principales: Alabdaljabar, Mohamad S, Hasan, Babar, Noseworthy, Peter A, Maalouf, Joseph F, Ammash, Naser M, Hashmi, Shahrukh K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891080/
https://www.ncbi.nlm.nih.gov/pubmed/36741292
http://dx.doi.org/10.2147/JMDH.S383810
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author Alabdaljabar, Mohamad S
Hasan, Babar
Noseworthy, Peter A
Maalouf, Joseph F
Ammash, Naser M
Hashmi, Shahrukh K
author_facet Alabdaljabar, Mohamad S
Hasan, Babar
Noseworthy, Peter A
Maalouf, Joseph F
Ammash, Naser M
Hashmi, Shahrukh K
author_sort Alabdaljabar, Mohamad S
collection PubMed
description Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients’ lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.
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spelling pubmed-98910802023-02-02 Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries Alabdaljabar, Mohamad S Hasan, Babar Noseworthy, Peter A Maalouf, Joseph F Ammash, Naser M Hashmi, Shahrukh K J Multidiscip Healthc Review Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients’ lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs. Dove 2023-01-28 /pmc/articles/PMC9891080/ /pubmed/36741292 http://dx.doi.org/10.2147/JMDH.S383810 Text en © 2023 Alabdaljabar et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Review
Alabdaljabar, Mohamad S
Hasan, Babar
Noseworthy, Peter A
Maalouf, Joseph F
Ammash, Naser M
Hashmi, Shahrukh K
Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries
title Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries
title_full Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries
title_fullStr Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries
title_full_unstemmed Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries
title_short Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries
title_sort machine learning in cardiology: a potential real-world solution in low- and middle-income countries
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891080/
https://www.ncbi.nlm.nih.gov/pubmed/36741292
http://dx.doi.org/10.2147/JMDH.S383810
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