IMR Press / RCM / Volume 23 / Issue 8 / DOI: 10.31083/j.rcm2308256
Open Access Review
Artificial Intelligence in Echocardiography: The Time is Now
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1 Department of Cardiology, Fiona Stanley Hospital, WA 6150 Murdoch, Australia
2 Department of Allied Health, Fiona Stanley Hospital, WA 6150 Murdoch, Australia
3 Curtin School of Allied Health, Curtin University, WA 6102 Bentley, Australia
4 Harry Perkins Institute of Medical Research, WA 6150 Murdoch, Australia
5 School of Medicine, The University of Western Australia, WA 6009 Crawley, Australia
6 School of Medicine, Curtin University, WA 6102 Bentley, Australia
7 Department of Cardiology, Royal Perth Hospital, WA 6000 Perth, Australia
*Correspondence: (Girish Dwivedi)
Academic Editor: Attila Nemes
Rev. Cardiovasc. Med. 2022, 23(8), 256;
Submitted: 27 March 2022 | Revised: 19 May 2022 | Accepted: 13 June 2022 | Published: 19 July 2022
(This article belongs to the Special Issue Role of Echocardiography in Current Cardiology Practice)
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Artificial Intelligence (AI) has impacted every aspect of clinical medicine, and is predicted to revolutionise diagnosis, treatment and patient care. Through novel machine learning (ML) and deep learning (DL) techniques, AI has made significant grounds in cardiology and cardiac investigations, including echocardiography. Echocardiography is a ubiquitous tool that remains first-line for the evaluation of many cardiovascular diseases, with large data sets, objective parameters, widespread availability and an excellent safety profile, it represents the perfect candidate for AI advancement. As such, AI has firmly made its stamp on echocardiography, showing great promise in training, image acquisition, interpretation and analysis, diagnostics, prognostication and phenotype development. However, there remain significant barriers in real-world clinical application and uptake of AI derived algorithms in echocardiography, most importantly being the lack of clinical outcome studies. While AI has been shown to match or even best its human counterparts, an improvement in real world outcomes remains to be established. There are also legal and ethical concerns that hinder its progress. Large outcome focused trials and a collaborative multi-disciplinary effort will be necessary to push AI into the clinical workspace. Despite this, current and emerging trials suggest that these systems will undoubtedly transform echocardiography, improving clinical utility, efficiency and training.

artificial intelligence
deep learning
machine learning
Fig. 1.
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