AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.8468
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ORIGINAL RESEARCH ARTICLE

Artificial intelligence in cardiac rhythm diagnostics and management: Challenges and opportunities

Robert Splinter1,2,3,4*
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1 Intelligent Bioinformatics Ltd, Thirsk, North Yorkshire, United Kingdom
2 Intelligent Bioinformatics LLC, Mooresville, North Carolina, United States of America
3 Departments of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
4 Departments of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
AIH, 8468 https://doi.org/10.36922/aih.8468
Submitted: 9 January 2025 | Revised: 3 March 2025 | Accepted: 12 March 2025 | Published: 2 April 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Each day, one million people undergo electrocardiogram diagnostics. The diagnostic process is time-consuming and often yields incomplete or inconclusive results, placing significant strain on physicians. Artificial intelligence (AI)-assisted diagnosis can significantly alleviate this burden by enhancing diagnostic accuracy and efficiency, and its application is gaining traction across various fields. With the increasing number of patients and a growing backlog of diagnostic appointments, AI can offer physicians benefits such as accurate, timely, and reliable assistance in reviewing vital signs and conducting physical examinations for individual patients. As physicians face mounting pressure from insurance companies and government guidelines for consultation time, AI can help streamline the diagnostic process. In particular, with the growing global attention on cardiac health (and the overall decline thereof), the range of automated diagnostic opportunities is expanding rapidly. Additional mathematical processing tools can provide probabilistic assessments of various cardiac conditions, reducing physicians’ workload while enhancing treatment options. AI has already demonstrated success in expediting the detection of pathological cardiac depolarization abnormalities and shortening diagnostic time frames. However, AI-based diagnostics requires further validation and safeguards to minimize diagnostic inaccuracies, ensuring its reliability and safety in clinical practice.

Keywords
Machine learning
Diagnostics
Medicine
Risk stratification
Screening
Signal-processing
Matched filter
Wavelet analysis
Funding
None.
Conflict of interest
The author declares no conflicts of interest.
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