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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms. The ever-increasing computational capacity of today’s computer systems enables more complex ML systems with millions of parameters, bringing AI closer to human intelligence. With this objective, the term deep learning (DL) has been introduced to characterize ML based on deep ANN (DNN) architectures with multiple layers of artificial neurons. Despite all of these promises, the impact of AI in current clinical practice is still limited. However, this could change shortly, as the significantly increased papers in AI, machine learning and deep learning in cardiology show. We highlight the significant achievements of recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take a central stage in the field.

Details

Title
Artificial Intelligence in Cardiology—A Narrative Review of Current Status
Author
Koulaouzidis, George 1 ; Jadczyk, Tomasz 2   VIAFID ORCID Logo  ; Iakovidis, Dimitris K 3 ; Koulaouzidis, Anastasios 4   VIAFID ORCID Logo  ; Bisnaire, Marc 5 ; Dafni Charisopoulou 6 

 Department of Biochemical Sciences, Pomeranian Medical University (PMU), 70-204 Szczecin, Poland; [email protected] 
 Division of Cardiology and Structural Heart Diseases, Medical University of Silesia, 40-551 Katowice, Poland; [email protected]; International Clinical Research Center, St. Anne’s University Hospital Brno, 656 91 Brno, Czech Republic 
 Department of Computer Science and Biomedical Informatics, University of Thessaly, 40500 Lamia, Greece; [email protected] 
 Department of Social Medicine & Public Health, Pomeranian Medical University (PMU), 70-204 Szczecin, Poland; Department of Medicine, OUH Svendborg Sygehus, 5700 Svendborg, Denmark; Surgical Research Unit, Odense University Hospital, 5000 Odense, Denmark; Department of Clinical Research, University of Southern Denmark (SDU), 5000 Odense, Denmark 
 Cardiology Research and Scientific Advancements, UVA Research, Toronto, ON L3R 3Z3, Canada; [email protected] 
 Academic Centre for Congenital Heart Disease, 6500 HB Nijmegen, The Netherlands; [email protected]; Amalia Children’s Hospital, Radboud University Medical Centre, 6525 GA Nijmegen, The Netherlands 
First page
3910
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20770383
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2686027719
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.