Bibliographic record
Abstract
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. Publication History Received: 27 April 2025 Accepted: 11 May 2025 Article published online: 03 July 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/) Thieme Medical and Scientific Publishers Pvt. Ltd. A-12, 2nd Floor, Sector 2, Noida-201301 UP, India
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".