AI-Driven ECG: The Smart Future of Cardiology

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]

 

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Understanding the AI Engine: Convolutional Neural Networks

AI models that analyze ECGs typically rely on convolutional neural networks (CNNs), a class of deep learning algorithms especially well-suited for recognizing patterns in visual or time-series data. In the context of ECG interpretation, CNNs process the raw waveform as a series of voltage-time signals and learn to identify subtle features that may be imperceptible to the human eye.

CNNs operate by passing the input ECG data through multiple layers, each designed to extract specific signal characteristics—such as wave shapes, intervals, or morphologic anomalies—and gradually build a complex internal representation of the data. These models are trained on large annotated ECG data sets, allowing them to “learn” to predict conditions like AF, LV dysfunction, or even electrolyte disturbances with impressive accuracy.

Unlike traditional rule-based algorithms, CNNs do not require explicit programming of features; instead, they automatically discover the most relevant patterns during the training process. This self-learning ability makes them uniquely powerful in predicting early or atypical presentations of cardiac conditions, positioning them as a transformative tool in cardiology diagnostics.[7]

Prediction Atrial Fibrillation Risk from Sinus Rhythm ECG

Approximately 20% of patients with AF are asymptomatic, and another third present with nonspecific symptoms like fatigue, dyspnea, or light headedness—factors that contribute to underdiagnosis. Additionally, up to 15% of cryptogenic stroke patients are later found to have undetected paroxysmal AF. Early identification or prediction of AF is therefore critical, as it enables timely anticoagulation therapy, potentially reducing stroke recurrence and mortality.[8]

Recent advances in AI have enabled ECG-based predictive modeling of AF, even in patients with normal sinus rhythm. AI algorithms, particularly CNNs, can detect subtle ECG changes such as P-wave morphology alterations and PR interval dynamics suggestive of atrial electrical remodeling or fibrosis.[9]

In a landmark 2019 study, Attia et al developed an AI-enabled ECG model that identified the electrocardiographic signature of AF during sinus rhythm using standard 10-second, 12-lead ECGs. The model demonstrated a sensitivity of about 79% and specificity of 81%.[9] Later studies confirmed that even single-lead ECGs, when analyzed using AI, can successfully predict future AF episodes—supporting its use as a noninvasive AF screening.[10]

Clinical Implications

AI-enhanced ECG analysis shows great potential in identifying high-risk individuals who might benefit from early intervention, such as anticoagulation for stroke prevention.

 

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Limitations

Conditions like structural heart disease (e.g., mitral stenosis or longstanding hypertension) can cause left atrial enlargement, mimicking AF-related changes. Age-related atrial remodeling in elderly patients may also affect prediction accuracy.[10]

 

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Prediction of Asymptomatic Left Ventricular Dysfunction

AI, particularly CNNs, has shown strong potential in detecting subtle ECG markers of LV dysfunction, even in asymptomatic individuals with normal-appearing ECGs. These markers may include minor QRS changes, T-wave abnormalities, or altered voltage patterns that typically escape human detection.

A landmark study by Attia et al (2019, Mayo Clinic) trained a CNN model on large data sets linking standard 12-lead ECGs with echocardiographic LV ejection fraction (LVEF). The algorithm accurately detected LVEF < 35%, achieving 86% sensitivity and 85% specificity.[5] Remarkably, it identified subclinical LV dysfunction in patients whose ECGs were deemed normal by physicians.

Clinical Implications

Early detection: Enables identification of LV dysfunction before symptom onset.

Screening tool: Especially valuable in primary care and resource-limited settings without echocardiography access.

Risk stratification: Helps prioritize patients for further cardiac imaging and specialist referral.[11]

 

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Detection of Hypertrophic Cardiomyopathy

AI-enhanced ECG models have demonstrated the ability to detect characteristic electrocardiographic signatures associated with HCM. These include increased voltage, repolarization abnormalities, and alterations in QRS morphology. In a study by Ko et al, the AI model achieved an impressive accuracy for detecting HCM from a 12-lead ECG, with a sensitivity of 95% and a specificity of 92%. Remarkably, the model retained high performance even in cases with subtle or borderline ECG abnormalities.[12]

Clinical Implications

A positive HCM prediction by AI should prompt confirmatory testing with echocardiography, cardiac magnetic resonance imaging, and genetic testing. This approach is particularly useful as a screening tool for asymptomatic individuals, especially those planning to participate in competitive or high-intensity sports, where undiagnosed HCM poses significant risk.

 

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Potential Sources of Error

Athlete's heart: Physiological hypertrophy in trained athletes may resemble HCM, potentially leading to false-positive results. Additionally, structural changes due to chronic hypertension (LV hypertrophy) can mimic HCM on ECG and may compromise AI model accuracy.[11] [12]

 

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Detection of Cardiac Amyloidosis

CA is associated with significant morbidity and mortality, often due to delays in diagnosis. Timely identification is crucial, particularly in light of emerging disease-modifying therapies that can improve patient outcomes when initiated early. There is, therefore, a growing clinical need for a widely accessible and cost-effective screening tool to facilitate early detection.[13]

CA leads to subtle electrocardiographic abnormalities that may precede the clinical diagnosis. AI-enabled analysis of standard ECGs has demonstrated the ability to detect these early changes, offering a promising avenue for earlier recognition of CA. Recent studies have shown that AI-enhanced ECG models can effectively identify patients with CA. In one such study, various configurations were evaluated, including both single-lead and 6-lead ECG subsets.[14]

Among single leads, lead V5 demonstrated the highest performance, with an area under the curve (AUC) of 0.86 and a precision of 0.78, while other single leads showed comparable efficacy. The 6-lead bipolar configuration yielded an even stronger performance, with an AUC of 0.90 and a precision of 0.85.[14]

These findings suggest that AI-driven ECG analysis holds considerable promise for the early, noninvasive detection of CA, potentially enabling earlier initiation of treatment and improving long-term prognosis.

 

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Prediction of Hyperkalemia

AI models can identify subtle and overt electrocardiographic features suggestive of hyperkalemia. These include peaked T waves, PR interval prolongation, QRS complex widening, and general T-wave abnormalities. These features may be recognized by the AI even when classical signs are not overtly apparent to the human eye.[15]

In a study by Galloway et al (2021), an AI-enabled model applied to standard 12-lead ECGs demonstrated excellent accuracy in detecting hyperkalemia. The model achieved a sensitivity of approximately 82% and a specificity of 88%.[16]

Clinical implications: The early prediction of hyperkalemia by AI-enhanced ECG analysis, particularly in patients with chronic kidney disease or acute renal failure, has significant clinical value. When an AI model flags possible hyperkalemia, immediate confirmation with serum potassium measurement is critical to prevent potentially life-threatening arrhythmias and guide prompt treatment.

 

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Conclusion

AI-enhanced ECG is rapidly transforming cardiovascular care by enabling accurate prediction of conditions such as reduced LVEF, AF, HCM, CA, and even electrolyte imbalances. Its integration into clinical practice promises earlier diagnosis, better risk stratification, and more personalized care. Although still evolving, AI-enhanced ECG analysis has demonstrated strong potential as a noninvasive, scalable tool that can improve patient outcomes and reduce strain on health care systems.

However, several limitations remain. The accuracy of AI algorithms may vary across different populations and clinical settings, highlighting the need for broader validation and real-world testing. Model interpretability, regulatory oversight, and integration with existing health care infrastructure are additional areas requiring refinement. Ethical concerns such as data privacy, potential algorithmic bias, and the risk of overreliance on automated interpretations must also be carefully addressed. Continued research, transparent model development, and responsible deployment will be essential to ensure that AI-enhanced ECG fulfills its promise in a safe, equitable, and clinically effective manner.

 

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Conflict of Interest

None declared.

Financial Support

None.

• References

• 1 Krikler DM. Historical aspects of electrocardiography. Cardiol Clin 1987; 5 (03) 349-355
• 2 Cook DA, Oh SY, Pusic MV. Accuracy of physicians' electrocardiogram interpretations: a systematic review and meta-analysis. JAMA Intern Med 2020; 180 (11) 1461-1471
• 3 Antiperovitch P, Zareba W, Steinberg JS. et al. Proposed in-training electrocardiogram interpretation competencies for undergraduate and postgraduate trainees. J Hosp Med 2018; 13 (03) 185-193
• 4 Organization. Global Status Report on Noncommunicable Diseases 2014: Attaining the Nine Global Noncommunicable Diseases Targets; A Shared Responsibility OCLC: 907517003. Geneva: World Health Organization; 2014
• 5 Attia ZI, Kapa S, Lopez-Jimenez F. et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 2019; 25 (01) 70-74
• 6 Duffy G. et al. Artificial intelligence for electrocardiography: history, current applications, and future directions. J Cardiol 2022; 79 (03) 213-225
• 7 Ribeiro AH, Ribeiro MH, Paixao GM. et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 1760; 2020: 11
• 8 Rienstra M, Lubitz SA, Mahida S. et al. Symptoms and functional status of patients with atrial fibrillation: state of the art and future research opportunities. Circulation 2012; 125 (23) 2933-2943
• 9 Attia ZI, Noseworthy PA, Lopez-Jimenez F. et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 2019; 394 (10201): 861-867
• 10 Gadaleta M, Harrington P, Barnhill E. et al. Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias. NPJ Digit Med 2023; 6 (01) 229
• 11 Bjerkén LV, Rønborg SN, Jensen MT, Ørting SN, Nielsen OW. Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review. Heart Fail Rev 2023; 28 (02) 419-430
• 12 Ko WY, Siontis KC, Attia ZI. et al. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram. J Am Coll Cardiol 2020; 75 (07) 722-733
• 13 Kittleson MM, Maurer MS, Ambardekar AV. et al; American Heart Association Heart Failure and Transplantation Committee of the Council on Clinical Cardiology. Cardiac amyloidosis: evolving diagnosis and management: a scientific statement from the American Heart Association. Circulation 2020; 142 (01) e7-e22
• 14 Grogan M, Lopez-Jimenez F, Cohen-Shelly M. et al. Artificial intelligence-enhanced electrocardiogram for the early detection of cardiac amyloidosis. Mayo Clin Proc 2021; 96 (11) 2768-2778
• 15 Harmon DM, Heinrich CK, Dillon JJ. et al. Mortality risk stratification utilizing artificial intelligence electrocardiogram for hyperkalemia in cardiac intensive care unit patients. JACC Adv 2024; 3 (09) 101169
• 16 Galloway CD, Valys AV, Shreibati JB. et al. Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol 2021; 4 (05) 428436

Publikationsverlauf

Eingereicht: 27. April 2025

Angenommen: 11. Mai 2025

Artikel online veröffentlicht:
03. Juli 2025