ReviewCurrent cardiology reports2023
The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation.
Review in Current cardiology reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis 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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Short-Term Arrhythmia Prediction Using AI Based on Daily Data From Implantable Devices: Multicenter Prospective Observational Study.JMIR cardio · 2026Observational
- Artificial intelligence in arrhythmia risk prediction: connecting undetectable vulnerabilities and proactive care.Annals of medicine and surgery (2012) · 2026Article
- AI-Powered Precision: Revolutionizing Atrial Fibrillation Detection with Electrocardiograms.Journal of clinical medicine · 2025Review
- [Prediction method of paroxysmal atrial fibrillation based on multimodal feature fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Validation, bias assessment, and optimization of the UNAFIED 2-year risk prediction model for undiagnosed atrial fibrillation using national electronic health data.Heart rhythm O2 · 2024Article
- AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions.Diagnostics (Basel, Switzerland) · 2024Review
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Article
- AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CACJournal of cardiovascular computed tomographyArticle
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewAtrial fibrillation (AF) is a major public health problem associated with preventable morbidity. Artificial intelligence (AI) is emerging as potential tool to prioritize individuals at increased risk for AF for preventive interventions. This review summarizes recent advances in the use of AI models to estimate AF risk. RECENT
findingsSeveral AI-enabled models have been recently developed which can discriminate AF risk with reasonable accuracy. AI models utilizing the electrocardiogram waveform appear to extract predictive information which is additive beyond traditional clinical risk factors. By identifying individuals at higher risk for AF, AI-based models may improve the efficiency of preventive efforts (e.g., screening, risk factor modification) intended to reduce risk of AF and associated morbidity.
Indexed as
Identifiers
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.