ReviewCirculation research2021
Machine Learning in Arrhythmia and Electrophysiology.
Review in Circulation research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
62 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.Current cardiology reviews · 2025Pooled it
- Clinician Performance in Training Data Curation for an Arrhythmia Machine Learning Model: Is Anyone Qualified?CJC pediatric and congenital heart disease · 2026Article
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- A deep learning algorithm for coronary heart disease prediction based on retinal fundus photographs and optical coherence tomography.BMC medical imaging · 2026Article
- Review
- Artificial Intelligence for Perioperative Risk Prediction and Prevention in Cardiac Surgery: A Narrative Review and Proposed Conceptual Framework.Journal of clinical medicine · 2026Review
- Predicting ventricular arrhythmia in myocardial ischemia using deep learning.Heart rhythm O2 · 2026Article
- Aspiration to architecture: multi-omics, AI, digital twins, and blockchain for P4 medicine.Journal of translational medicine · 2026Article
- Self-supervised contrastive learning enables robust electrocardiogram-based cardiac classification.Heart rhythm O2 · 2026Article
- Efficacy and safety of ventricular tachycardia ablation versus antiarrhythmics in ischemic cardiomyopathy: a network meta-analysis.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2026Article
- Temporal Variability and Influence of Measurement Conditions of AI-Based Atrial Fibrillation Risk Estimation.Journal of arrhythmia · 2026Article
- Learning cardiac activation and repolarization times with operator learning.PLoS computational biology · 2026Article
- Review
- Article
- From Innovation to Integration: Bridging the Gap Between IoMT Technologies and Real-World Health Management Systems.Sensors (Basel, Switzerland) · 2025Article
- Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.International journal of cardiology. Heart & vasculature · 2025Review
- CRT-D or CRT-P: When There Is a Dilemma and How to Solve It.Journal of clinical medicine · 2025Review
- Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions.Reviews in cardiovascular medicine · 2025Review
- The Electrophysiology Lab of the Future.The Journal of innovations in cardiac rhythm management · 2025Article
2 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
3 authors.
Funding
Abstract
Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.
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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.