ReviewOpen heart2026
AI-enhanced electrocardiography as a digital biomarker platform in cardiovascular medicine: clinical applications, validation gaps and future implementation pathways.
Review in Open heart, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- CardioSynergyNet: A Closed-Loop Multi-Task Deep Learning Architecture for Cardiac Segmentation and Biomarkers with Diagnosis from Paired ED-ES Cine-MRI.Tomography (Ann Arbor, Mich.) · 2026Article
- Effects of Use of Primary Care Checklists and of Extent of Clinical Experience on Performance in Interpreting Paediatric ECGs Linked to Risk of Sudden Cardiac Death.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence applied to the ECG is expanding the clinical role of this widely available diagnostic tool beyond conventional waveform interpretation by enabling identification of electrophysiological patterns associated with cardiovascular structure, function and risk. Within this evolving framework, the artificial intelligence enhanced ECG is emerging as a scalable digital biomarker platform supporting screening, diagnosis and prognostic stratification across the cardiovascular disease continuum.This narrative review synthesises current evidence on the clinical applications of artificial intelligence enhanced ECG, including detection of cardiac rhythm disorders, early identification of structural heart disease, decision support in acute coronary syndromes, prediction of clinical outcomes and population level cardiovascular screening using wearable technologies. Deep learning models applied to standard and simplified electrocardiographic recordings demonstrate strong diagnostic performance in identifying atrial fibrillation, left ventricular dysfunction, hypertrophic cardiomyopathy, cardiac amyloidosis and heart failure with preserved ejection fraction. These approaches also enable estimation of long-term cardiovascular risk from apparently normal tracings and support identification of disease patterns extending beyond overt cardiac conditions.Despite these advances, clinical implementation remains limited by the need for prospective multicentre validation, improved model interpretability, standardised evaluation strategies and integration into routine clinical workflows. This review highlights current validation gaps and outlines key opportunities for translation of artificial intelligence-enhanced ECG into digital cardiology practice.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.