Translational maturity of artificial intelligence-enabled electrocardiography varies across cardiovascular diseases
- 1Department of Biomedical Engineering, Oriental Pan-Vascular Devices Innovation College, University of Shanghai for Science and Technology
- 2Department of Cardiology, School of Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University
Abstract
Electrocardiography is a widely available and low-cost cardiovascular test, and artificial intelligence-enabled electrocardiography (AI-ECG) can identify latent electrical phenotypes beyond conventional interpretation. However, high retrospective discrimination does not necessarily translate into clinically reliable or actionable benefit. This critical narrative review evaluates the translational maturity of AI-ECG across arrhythmias, acute myocardial infarction, heart failure, valvular heart disease, hypertrophic cardiomyopathy, and cardiac amyloidosis. PubMed, IEEE Xplore, and Web of Science were searched from January 1, 2022, through July 10, 2026, with earlier landmark studies added by backward citation tracking. Seventy-seven publications informed the review. Evidence was appraised using five clinically oriented criteria: intended use, reference-standard reliability, external validation and transportability, clinical actionability, and effects on workflow or patient outcomes. AI-ECG has progressed furthest in ST-segment elevation myocardial infarction triage and reduced-left-ventricular-ejection-fraction screening, where prospective or randomized studies have demonstrated workflow or diagnostic-yield benefits, whereas acute myocardial infarction rule-out has reached prospective multicenter diagnostic validation. Hypertrophic cardiomyopathy screening has also advanced through international validation and prospective implementation. By contrast, occlusion myocardial infarction detection, prediction of occult or future atrial fibrillation, valvular heart disease, heart failure with preserved ejection fraction, long-term heart-failure prediction, and cardiac amyloidosis remain primarily validation-stage or risk-enrichment applications. Across domains, broader adoption is constrained by label quality, calibration, transportability, operating-threshold selection, and limited patient-outcome evidence. AI-ECG is therefore best positioned as clinician-supervised decision support linked to explicit downstream testing and care pathways.
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BibTeX
@article{chen2026translational,
title = {{Translational maturity of artificial intelligence-enabled electrocardiography varies across cardiovascular diseases}},
author = {Huanhuan Chen and Jingjuan Huang and Xumin Hou},
journal = {Frontiers in Cardiovascular Medicine},
year = {2026},
month = oct,
doi = {10.3389/fcvm.2026.1940621},
url = {https://www.frontiersin.org/articles/10.3389/fcvm.2026.1940621/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Translational maturity of artificial intelligence-enabled electrocardiography varies across cardiovascular diseases
AU - Huanhuan Chen
AU - Jingjuan Huang
AU - Xumin Hou
PY - 2026
DA - 2026/10/16
JO - Frontiers in Cardiovascular Medicine
DO - 10.3389/fcvm.2026.1940621
UR - https://www.frontiersin.org/articles/10.3389/fcvm.2026.1940621/full
AB - Electrocardiography is a widely available and low-cost cardiovascular test, and artificial intelligence-enabled electrocardiography (AI-ECG) can identify latent electrical phenotypes beyond conventional interpretation. However, high retrospective discrimination does not necessarily translate into clinically reliable or actionable benefit. This critical narrative review evaluates the translational maturity of AI-ECG across arrhythmias, acute myocardial infarction, heart failure, valvular heart disease, hypertrophic cardiomyopathy, and cardiac amyloidosis. PubMed, IEEE Xplore, and Web of Science were searched from January 1, 2022, through July 10, 2026, with earlier landmark studies added by backward citation tracking. Seventy-seven publications informed the review. Evidence was appraised using five clinically oriented criteria: intended use, reference-standard reliability, external validation and transportability, clinical actionability, and effects on workflow or patient outcomes. AI-ECG has progressed furthest in ST-segment elevation myocardial infarction triage and reduced-left-ventricular-ejection-fraction screening, where prospective or randomized studies have demonstrated workflow or diagnostic-yield benefits, whereas acute myocardial infarction rule-out has reached prospective multicenter diagnostic validation. Hypertrophic cardiomyopathy screening has also advanced through international validation and prospective implementation. By contrast, occlusion myocardial infarction detection, prediction of occult or future atrial fibrillation, valvular heart disease, heart failure with preserved ejection fraction, long-term heart-failure prediction, and cardiac amyloidosis remain primarily validation-stage or risk-enrichment applications. Across domains, broader adoption is constrained by label quality, calibration, transportability, operating-threshold selection, and limited patient-outcome evidence. AI-ECG is therefore best positioned as clinician-supervised decision support linked to explicit downstream testing and care pathways.
SN - 2297-055X
ER -