ReviewJournal of cardiovascular electrophysiology2022
Artificial intelligence and atrial fibrillation.
Review in Journal of cardiovascular electrophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Systematic, randomized atrial fibrillation screening using detailed phenotyping with a risk prediction model combined with patch electrocardiogram in a Swedish population aged 65 years or older: the CONSIDERING-AF trial.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 · 2025Trial
- Racial and Ethnic Disparities in the Utilization and Outcomes of Catheter Ablation for Atrial Fibrillation and Flutter: a State-of-the-Art Review.Current cardiology reports · 2026Review
- Prognostic Significance of AI-Enhanced ECG for Emergency Department Patients.Diagnostics (Basel, Switzerland) · 2025Article
- AI-Powered Precision: Revolutionizing Atrial Fibrillation Detection with Electrocardiograms.Journal of clinical medicine · 2025Review
- Article
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
- The Pathophysiology of Sex Differences in Stroke Risk and Prevention in Atrial Fibrillation: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2025Review
- An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.JMIR human factors · 2025Article
- The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation.Cureus · 2025Review
- Advances in Atrial Fibrillation Management: A Guide for General Internists.Journal of clinical medicine · 2024Review
- Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review.Sports (Basel, Switzerland) · 2024Review
- COMP Improves Ang-II-Induced Atrial Fibrillation via TGF-β Signaling Pathway.Cardiovascular toxicology · 2023Article
- Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation.Arrhythmia & electrophysiology review · 2023Review
- Wearable Devices Combined with Artificial Intelligence-A Future Technology for Atrial Fibrillation Detection?Sensors (Basel, Switzerland) · 2022Article
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
backgroundIn the context of atrial fibrillation (AF), traditional clinical practices have thus fallen short in several domains, such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems.
aimsTo discuss the roles of artificial intelligence (AI)-enabled electrocardiogram (ECG) pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. MATERIALS &
methodsAn extensive search and review of the currently available literature on the topics.
resultsOne key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Challenges with regards to the benefits and harms of AF screening remain. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm. DISCUSSION: Knowledge gaps remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and identifying those who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. The role of DL models assessing AF burden from long-duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, the role of adequate external validation of the models and clinical trials to study true performance is discussed.
conclusionAlgorithms using AI to interpret ECGs in various new ways have been developed. While still, much work needs to be done, these technologies have shown enormous potential in a short span of time. With further advancements and continuous research, these novel ways of interpretation may well become part of everyday clinical workflow.
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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.