ReviewCardiovascular research2021
How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.
Review in Cardiovascular research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.Current cardiology reviews · 2025Pooled it
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.NPJ digital medicine · 2026Article
- Article
- Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning model for predicting in-hospital cardiac mortality among atrial fibrillation patients.Scientific reports · 2025Article
- Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.JACC. Asia · 2025Article
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- A machine learning-based risk prediction model for atrial fibrillation in critically ill patients.Heart rhythm O2 · 2025Article
- Atrial fibrillation in critical illness: state of the art.Intensive care medicine · 2025Review
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 2025Article
- Machine learning to identify phenotypic clusters of patients with atrial fibrillation.Heart rhythm O2 · 2025Article
- Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations.Frontiers in cardiovascular medicine · 2025Review
- Postoperative Atrial Fibrillation: A Review.Biomedicines · 2024Review
- Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.BMC primary care · 2024Article
- Post-Ischemic Stroke Cardiovascular Risk Prevention and Management.Healthcare (Basel, Switzerland) · 2024Review
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- [Digital precision medicine in rhythmology : Risk prediction of recurrences, sudden cardiac death, and outcome].Herzschrittmachertherapie & Elektrophysiologie · 2024Review
- Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study.European heart journal. Digital health · 2024Article
- Sex-specific associations between the environmental exposures and low-grade inflammation and increased blood pressure in young, healthy subjects.Scientific reports · 2024Article
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
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable 'real time' dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate 'real time' assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.
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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.