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CC BY 4.0 · Libyan International Medical University Journal
DOI: 10.1055/s-0045-1809615
Commentary

AI-Driven ECG: The Smart Future of Cardiology

Hassan A. Gargoum
1   University of Saskatchewan, Regina General Hospital, Regina, Canada
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Introduction

For over a century, the electrocardiogram (ECG) has been a cornerstone of cardiovascular diagnostics—offering a noninvasive, accessible, and rapid assessment of cardiac electrical activity. It remains vital in detecting arrhythmias, myocardial infarction, conduction abnormalities, and structural heart diseases. Yet, its interpretation has traditionally depended on clinician expertise, which can lead to inconsistent accuracy.[1] [2]

Studies reveal that nearly one-third of ECG readings contain major errors. A 2020 meta-analysis found a median interpretation accuracy of only 54% among physicians, rising modestly to 67% after educational interventions. These persistent gaps highlight the limitations of human interpretation despite training efforts.[3]

Such challenges have fueled interest in more advanced solutions. [Table 1] contrasts traditional ECG interpretation with artificial intelligence (AI)-driven approaches in terms of accuracy, scalability, and clinical relevance. The need for automated ECG analysis is particularly pressing in low- and middle-income countries, where over 75% of global cardiovascular deaths occur and access to expert cardiologists is limited.[4]

Table 1

Comparison of traditional versus AI-driven ECG interpretation

Feature

Traditional ECG analysis

AI-driven ECG analysis

Interpretation speed

Minutes to hours

Seconds

Interobserver variability

High

Minimal

Diagnostic accuracy

Depends on clinician expertise

High (if trained on robust data)

Detection of subtle abnormalities

Limited

Enhanced sensitivity

Continuous monitoring

Not feasible

Enabled with wearable AI devices

Abbreviations; AI, artificial intelligence; ECG, electrocardiogram.


Recent advances in AI, especially deep learning, are reshaping ECG analysis. AI algorithms can process vast data sets, detect subtle patterns beyond human perception, and deliver highly accurate predictive insights. These tools have demonstrated promise in diagnosing latent or asymptomatic conditions such as left ventricular (LV) dysfunction, atrial fibrillation (AF), hypertrophic cardiomyopathy (HCM), and cardiac amyloidosis (CA)—often before symptoms emerge ([Table 2]).

Table 2

Current AI applications in ECG and their clinical utility

AI application

Clinical utility/Significance

Arrhythmia detection (e.g., AFib, VT, PVCs)

Improves diagnostic accuracy and early detection of arrhythmias often in asymptomatic patients. Enables timely intervention and reduces stroke risk

ECG interpretation assistance

Enhances efficiency and consistency in reading ECGs, especially in high-volume settings. Reduces interobserver variability

Prediction of left ventricular dysfunction

AI models can detect reduced ejection fraction (e.g., LVEF < 40%) from surface ECGs alone, facilitating early heart failure diagnosis even before symptoms or echo abnormalities appear

Detection of silent myocardial ischemia or infarction

AI-enhanced ECG can identify subtle patterns indicative of ischemia or prior MI not recognized by standard interpretation, especially useful in diabetics or atypical cases

Hyperkalemia or hypokalemia prediction

Detects electrolyte disturbances from ECG patterns before lab confirmation, allowing quicker clinical decision-making

Risk stratification (e.g., sudden cardiac death, AFib recurrence)

Identifies patients at higher risk for adverse events and guides monitoring or therapy escalation. For example, predicting need for ICD in nonischemic cardiomyopathy

Disease screening in asymptomatic populations

Facilitates mass screening for conditions like hypertrophic cardiomyopathy, AFib, or heart failure with preserved EF (HFpEF)

Remote monitoring and wearable integration

AI enables continuous rhythm monitoring from smartwatches or patches, filtering noise and detecting actionable events with high accuracy

Early detection of noncardiac conditions

Emerging use of AI to predict conditions like sleep apnea, anemia, and even COVID-19 through ECG pattern analysis

Abbreviations; AFib, atrial fibrillation; AI, artificial intelligence; COVID-19, coronavirus disease 2019; ECG, electrocardiogram; HFpEF, heart failure with preserved ejection fraction; ICD, implantable cardioverter-defibrillator; LVEF, left ventricular ejection fraction; MI, myocardial infarction; PVC, premature ventricular contraction; VT, ventricular tachycardia.


AI, particularly machine learning and deep neural networks, is rapidly becoming a transformative force in cardiology. The following sections explore key clinical applications where AI-enhanced ECG has shown significant diagnostic and prognostic value.[5] [6]

Financial Support

None.




Publikationsverlauf

Eingereicht: 27. April 2025

Angenommen: 11. Mai 2025

Artikel online veröffentlicht:
03. Juli 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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