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Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Chung, Cheuk To, Lee, Sharen, King, Emma, Liu, Tong, Armoundas, Antonis A., Bazoukis, George, Tse, Gary (2022) Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis. International Journal of Arrhythmia, (23). Article Number 24 (2022). ISSN 2466-1171. (doi:10.1186/s42444-022-00075-x) (KAR id:98703)

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Official URL:
https://doi.org/10.1186/s42444-022-00075-x

Abstract

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.

Item Type: Article
DOI/Identification number: 10.1186/s42444-022-00075-x
Uncontrolled keywords: Electrocardiography; Artifcial intelligence; Machine learning; Deep learning; Cardiovascular diseases
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
Depositing User: Manfred Gschwandtner
Date Deposited: 05 Dec 2022 18:34 UTC
Last Modified: 07 Dec 2022 16:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/98703 (The current URI for this page, for reference purposes)
Tse, Gary: https://orcid.org/0000-0001-5510-1253
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