Please use this identifier to cite or link to this item: https://dora.health.qld.gov.au/qldresearchjspui/handle/1/6297
Title: Application of Artificial Intelligence in Cardiology: A Bibliometric Analysis
Authors: Sidik, Abubakar I
Komarov, Roman N
Gawusu, Sidique
Moomin, Aliu
Al-Ariki, Malik K
Elias, Marina
Sobolev, Dmitriy
Karpenko, Ivan G
Esion, Grigorii
Akambase, Jonas 
Dontsov, Vladislav V
Mohammad Shafii, Abdul Majed I
Ahlam, Derrar
Arzouni, Naya W
Issue Date: 2024
Source: Sidik AI, Komarov RN, Gawusu S, Moomin A, Al-Ariki MK, Elias M, Sobolev D, Karpenko IG, Esion G, Akambase J, Dontsov VV, Mohammad Shafii AMI, Ahlam D, Arzouni NW. Application of Artificial Intelligence in Cardiology: A Bibliometric Analysis. Cureus. 2024 Aug 15;16(8):e66925. doi: 10.7759/cureus.66925. PMID: 39280440; PMCID: PMC11401640.
Journal Title: Cureus
Journal: Cureus
Abstract: Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.
Description: Cairns & Hinterland Hospital and Health Service (CHHHS) affiliated author: Jonas Akambase
DOI: 10.7759/cureus.66925
Keywords: machine learning;heart;deep learning;convolutional neural network;cardiology;cardiac;bibliometric analysis;artificial intelligence
Type: Article
Appears in Sites:Cairns & Hinterland HHS Publications

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