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Artificial Intelligence in Health

ORIGINAL RESEARCH ARTICLE
Artificial intelligence in cardiac rhythm

diagnostics and management: Challenges and
opportunities

Robert Splinter 1,2,3,4 *

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

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
*Corresponding author: reviewing vital signs and conducting physical examinations for individual patients.
Robert Splinter As physicians face mounting pressure from insurance companies and government
(rsplinter@ibi-gb.com) guidelines for consultation time, AI can help streamline the diagnostic process.
Citation: Splinter R. Artificial In particular, with the growing global attention on cardiac health (and the overall
intelligence in cardiac rhythm decline thereof), the range of automated diagnostic opportunities is expanding
diagnostics and management: rapidly. Additional mathematical processing tools can provide probabilistic
Challenges and opportunities. Artif
Intell Health. 2025;2(3):107-124. assessments of various cardiac conditions, reducing physicians’ workload while
doi: 10.36922/aih.8468 enhancing treatment options. AI has already demonstrated success in expediting
Received: January 9, 2025 the detection of pathological cardiac depolarization abnormalities and shortening
diagnostic time frames. However, AI-based diagnostics requires further validation
Revised: March 3, 2025
and safeguards to minimize diagnostic inaccuracies, ensuring its reliability and safety
Accepted: March 12, 2025 in clinical practice.
Published online: April 2, 2025
Copyright: © 2025 Author(s). Keywords: Machine learning; Diagnostics; Medicine; Risk stratification; Screening; Signal-
This is an Open-Access article processing; Matched filter; Wavelet analysis
distributed under the terms of the
Creative Commons Attribution
License, permitting distribution,
and reproduction in any medium,
provided the original work is
properly cited. 1. Introduction
Publisher’s Note: AccScience Data from four large population-based registries, which contain emergency medical
Publishing remains neutral with service data collected between 2012 and 2017 across major European Union (EU)
regard to jurisdictional claims in 1
published maps and institutional countries, report at least 450,000 cases of sudden cardiac death (SCD) annually.
affiliations. Similarly, heart failure poses a significant economic burden on the United States (US),

Volume 2 Issue 3 (2025) 107 doi: 10.36922/aih.8468