ReviewNature reviews. Cardiology2021
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.
Review in Nature reviews. Cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07774689 (Utilization of AI-ECG for Preoperative Cardiovascular Risk Stratification in General Surgery), which is not on this map. Cited by 386 papers, 4 of them syntheses that pooled it.
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.
Utilization of AI-ECG for Preoperative Cardiovascular Risk Stratification in General Surgery: A Randomized Controlled Trial
Who cites it
386 citing papers in PubMed, 4 syntheses or guidelines pooled it, 810 citations in OpenAlex.
- From assistance to autonomy: AI agent systems in cardiovascular medicine-a review of paradigms, architectures, and clinical translation.Frontiers in cardiovascular medicine · 2026Pooled it
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Trends in AI-based diagnosis and intervention of metabolic diseases: a bibliometric analysis of the literature from 2000 to 2024.Frontiers in medicine · 2025Pooled it
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Artificial Intelligence-Enabled ECGs for Atrial Fibrillation Identification and Enhanced Oral Anticoagulant Adoption: A Pragmatic Randomized Clinical Trial.Journal of the American Heart Association · 2025Trial
- Language-supervised electrocardiogram interpretation with vision transformer.Health information science and systems · 2026Article
- Multimodal artificial intelligence to predict cardiomyopathy development in hereditary transthyretin amyloidosis.Open heart · 2026Article
- Explainable age prediction using a transformer trained on diverse global electrocardiograms.European heart journal. Digital health · 2026Article
- Artificial intelligence-enabled electrocardiography for detection of left ventricular diastolic dysfunction: a systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Application of Artificial Intelligence and Machine Learning in Arrhythmia Detection and Pacemaker Data Analysis.Cardiology research · 2026Review
- Diagnostic performance of the ECG-SMART AI score for detecting angiographically significant coronary artery disease in patients undergoing elective coronary angiography.PLOS digital health · 2026Article
- Transportability of an artificial intelligence electrocardiography model for structural heart disease: external validation and recalibration of EchoNext-Mini in MIMIC-IV.European heart journal. Digital health · 2026Article
- Diagnostic Accuracy of Artificial Intelligence-Based Electrocardiography for the Detection of Heart Diseases: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Wearable Devices in Cardiovascular Care: A Narrative Review of the Transition Toward Predictive, Preventive, Personalized, and Participatory Medicine.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Echocardiography for Acute Coronary Syndromes: Current Evidence and Integration With Electrocardiography and Biomarkers.Echocardiography (Mount Kisco, N.Y.) · 2026Review
- Artificial intelligence and biomarker-driven prediction of post-coronary artery bypass grafting atrial fibrillation: Integrating clinical, genomic, and metabolic insights.Heart rhythm O2 · 2026Review
- A Review of the Integration of Artificial Intelligence in Cardiac Electrophysiology.Journal of clinical medicine · 2026Review
- Physicians' perspectives on artificial intelligence in electrocardiography in clinical practice: a qualitative study.BMC medical ethics · 2026Article
- Clinical Specialty Expansion of AI-Enabled and Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration From 1995 to 2025: Longitudinal Content Analysis.Journal of medical Internet research · 2026Article
- Inkjet-Printed Aqueous Graphene Suspensions on Flexible Substrates for Multimodal Wearable Continuous Health Monitoring.ACS nanoscience Au · 2026Article
326 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person's age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.
Indexed as
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.