ReviewFrontiers in cardiovascular medicine2024
Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives.
Review in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of ECG smart chest patches versus PPG smartwatches for atrial fibrillation detection: a systematic review and meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- The multiple uses of artificial intelligence in exercise programs: a narrative review.Frontiers in public health · 2025Pooled it
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- From Chaos to Care: Personalized AI for Early Cardiac Arrhythmia Warning.Machine learning with applications · 2026Article
- De-risking Atrial Fibrillation: Refining Anticoagulation Decision-Making.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Consumer Smartwatch Technology in Health and Performance Research: Validity, Limitations, and Real-World Applications.Sensors (Basel, Switzerland) · 2026Review
- Integrating AI-Driven Diagnostics in Arrhythmia Care to Enhance Patient Outcomes: A Narrative Review.Cureus · 2026Review
- A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study.JMIR mHealth and uHealth · 2026Observational
- Within-person modeling of postprandial glucose using multimodal wearable data.Frontiers in digital health · 2026Article
- Exploring nurse- and allied health professional-led opportunistic atrial fibrillation screening with artificial intelligence-enabled devices in community and primary care.Frontiers in cardiovascular medicine · 2026Review
- Comprehensive risk stratification model for sudden death in hypertrophic cardiomyopathy: integration of cardiac magnetic resonance and clinical parameters.Frontiers in cardiovascular medicine · 2026Article
- Monitoring Atrial Fibrillation Using Wearable Digital Technologies: The Emerging Role of Smartwatches.Journal of clinical medicine · 2025Review
- Diagnostic Accuracy of Wearable ECG Devices for Atrial Fibrillation and ST-Segment Changes: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- An Edge AI Approach for Low-Power, Real-Time Atrial Fibrillation Detection on Wearable Devices Based on Heartbeat Intervals.Sensors (Basel, Switzerland) · 2025Article
- Improving patients' satisfaction through service digitalization: a cross-sectional study based on the theory of psychological empowerment.BMC health services research · 2025Article
- Expert Review of the Strategies to Optimize Long-Term Outcomes After Coronary Artery Bypass Grafting.Reviews in cardiovascular medicine · 2025Review
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- Continuous biosignal acquisition beyond the limit of epidermal turnover.Materials horizons · 2025Review
- A Conceptual Framework for User Trust in AI Biosensors: Integrating Cognition, Context, and Contrast.Sensors (Basel, Switzerland) · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis.
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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.