SynthesisHeart failure reviews2023
Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review.
Synthesis in Heart failure reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Diagnosis of heart failure with preserved ejection fraction: a systematic narrative review of the evidence.Heart failure reviews · 2024Pooled it
- Review
- Prediction of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Based Electrocardiogram Analysis Model in Patients Presenting to the Emergency Department.Diagnostics (Basel, Switzerland) · 2026Article
- Developing and validating an artificial intelligence-based electronic triage model for predicting clinical outcomes among cardiac-suspected patients in the emergency department.European heart journal. Digital health · 2026Article
- Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease.Current heart failure reports · 2026Review
- Development and temporal validation of a two-stage ECG-based machine learning model for LVEF screening: a Middle Eastern cohort study.BMC cardiovascular disorders · 2026Article
- Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification.Current heart failure reports · 2026Review
- Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography.Journal of cardiovascular imaging · 2026Article
- Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction.JACC. Advances · 2026Article
- Web-Based Application of Simplified Machine Learning for Detecting Reduced LVEF From 12-Lead ECG.Journal of arrhythmia · 2026Article
- Prediction of left ventricular systolic dysfunction in left bundle branch block using a fine-tuned ECG foundation model.Scientific reports · 2026Article
- A Screening Method for Determining Left Ventricular Systolic Function Based on Spectral Analysis of a Single-Channel Electrocardiogram Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-enabled electrocardiography for assessment of left ventricular systolic dysfunction in the era of foundation models.Frontiers in cardiovascular medicine · 2026Review
- AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions.International journal of general medicine · 2026Review
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- Enhanced prediction of left ventricular ejection fraction using electrocardiography with the addition of clinical metadata.The Korean journal of internal medicine · 2026Article
- A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary function.NEJM AI · 2025Article
- Reliability of Artificial Intelligence-enhanced Electrocardiography.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms.JAMA cardiology · 2025Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Screening for left ventricular systolic dysfunction (LVSD), defined as reduced left ventricular ejection fraction (LVEF), deserves renewed interest as the medical treatment for the prevention and progression of heart failure improves. We aimed to review the updated literature to outline the potential and caveats of using artificial intelligence-enabled electrocardiography (AIeECG) as an opportunistic screening tool for LVSD.We searched PubMed and Cochrane for variations of the terms "ECG," "Heart Failure," "systolic dysfunction," and "Artificial Intelligence" from January 2010 to April 2022 and selected studies that reported the diagnostic accuracy and confounders of using AIeECG to detect LVSD.Out of 40 articles, we identified 15 relevant studies; eleven retrospective cohorts, three prospective cohorts, and one case series. Although various LVEF thresholds were used, AIeECG detected LVSD with a median AUC of 0.90 (IQR from 0.85 to 0.95), a sensitivity of 83.3% (IQR from 73 to 86.9%) and a specificity of 87% (IQR from 84.5 to 90.9%). AIeECG algorithms succeeded across a wide range of sex, age, and comorbidity and seemed especially useful in non-cardiology settings and when combined with natriuretic peptide testing. Furthermore, a false-positive AIeECG indicated a future development of LVSD. No studies investigated the effect on treatment or patient outcomes.This systematic review corroborates the arrival of a new generic biomarker, AIeECG, to improve the detection of LVSD. AIeECG, in addition to natriuretic peptides and echocardiograms, will improve screening for LVSD, but prospective randomized implementation trials with added therapy are needed to show cost-effectiveness and clinical significance.
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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.