ReviewFrontiers in cardiovascular medicine2025
Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations.
Review in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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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
22 citing papers in PubMed.
- Artificial Intelligence for Precision Antiarrhythmic Drug Therapy in Atrial Fibrillation: From Recurrence Prediction to Comparative Treatment Selection.Medicina (Kaunas, Lithuania) · 2026Review
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- Time Is Aorta: Can Artificial Intelligence Improve Surgical Timelines in Acute Type A Aortic Dissection? A Comprehensive Review.Health science reports · 2026Article
- Development and Validation of a Neural Network Model for Predicting Atrial Fibrillation and Detecting Silent Arrhythmias in Patients with Chronic Obstructive Pulmonary Disease Based on Echocardiography Data.Diseases (Basel, Switzerland) · 2026Article
- Prevention in stroke - Current state, present gaps and probable next steps.Neurological research and practice · 2026Review
- Device-Detected Atrial Fibrillation: Why Time-Based Thresholds Are No Longer Fit for Purpose.Journal of clinical medicine · 2026Article
- Artificial Intelligence Enabled Noninvasive Mapping of Cardiac Arrhythmia Origins Using the 12-Lead Electrocardiogram.Journal of cardiovascular electrophysiology · 2026Review
- Causal AI in Cardiac Arrhythmia: From Pattern Recognition to Mechanistic Insight.Clinical cardiology · 2026Review
- Artificial intelligence in arrhythmia risk prediction: connecting undetectable vulnerabilities and proactive care.Annals of medicine and surgery (2012) · 2026Article
- Advancements in artificial intelligence for the localization of premature ventricular contraction origins.Frontiers in cardiovascular medicine · 2026Review
- Using Orbital Atherectomy in ST-Elevation Myocardial Infarction: A Case Report From a Nonsurgical Centre.Case reports in cardiology · 2026Article
- Beyond the Biopsy: Imaging Frontiers in Cardiac Sarcoidosis.Cardiology research and practice · 2026Review
- AI-Enabled regional tele-ECG cloud platform and improving access to cardiovascular diagnosis: real-world evidence from southern China.Frontiers in cardiovascular medicine · 2026Article
- Digital Health-Enabled Monitoring Strategies for Atrial Fibrillation Detection after Embolic Stroke of Undetermined Source: A Cost-Effectiveness Analysis of Implantable Loop Recorders, Wearable Devices, and Usual Care.Cerebrovascular diseases (Basel, Switzerland) · 2026Article
- From polygenic risk to digital twins: the future of personalised cardiovascular medicine.Frontiers in cardiovascular medicine · 2026Review
- Pacemaker therapy through a sex-specific lens: a narrative review of clinical and procedural disparities.Frontiers in cardiovascular medicine · 2026Review
- Artificial Intelligence and the Future of Cardiac Implantable Electronic Devices: Diagnostics, Monitoring, and Therapy.Journal of clinical medicine · 2025Review
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning algorithms for predicting atrial fibrillation using single-lead data derived from 12-lead ECGs.Frontiers in cardiovascular medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is the most prevalent sustained arrhythmia and a major contributor to stroke and heart failure. Despite progress in management, challenges persist in early detection, risk stratification, and personalised treatment. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has emerged as a transformative tool in AF care. This scoping review examines the applications of AI across key domains: detection, risk prediction, treatment optimisation, and remote monitoring. AI-driven models enhance AF detection by analysing ECGs and wearable device data with high accuracy, enabling early identification of asymptomatic cases. By incorporating diverse clinical, imaging, and genomic data, predictive models outperform conventional risk scores in estimating stroke risk and disease progression. In treatment, AI assists in personalised anticoagulation decisions, catheter ablation planning, and optimising antiarrhythmic drug selection. Furthermore, AI-powered remote monitoring integrates wearable-derived insights with real-time decision support, improving patient engagement and adherence. Despite these advances, significant challenges persist, including algorithm transparency, bias, data integration, and regulatory hurdles. Explainable AI (XAI) is crucial to ensure clinician trust and facilitate implementation into clinical workflows. Future research should focus on large-scale validation, multi-modal data integration, and real-world AI deployment in AF management. AI has the potential to revolutionise AF care, shifting from reactive treatment to proactive, personalised management. Addressing current limitations through interdisciplinary collaboration will be key to realising AI's full potential in clinical practice and improving patient outcomes.
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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.