ReviewHerzschrittmachertherapie & Elektrophysiologie2022
Artificial intelligence for the detection, prediction, and management of atrial fibrillation.
Review in Herzschrittmachertherapie & Elektrophysiologie, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06001528 (Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer), which is not on this map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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.
Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Diagnostic Accuracy of Artificial Intelligence for Arrhythmia Detection Using the 12-Lead Electrocardiogram: A Systematic Review and Meta-Analysis.medRxiv : the preprint server for health sciences · 2026Article
- Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.International journal of cardiology. Heart & vasculature · 2025Review
- AI-Powered Precision: Revolutionizing Atrial Fibrillation Detection with Electrocardiograms.Journal of clinical medicine · 2025Review
- The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation.Cureus · 2025Review
- Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations.Frontiers in cardiovascular medicine · 2025Review
- 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
- Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.Scientific reports · 2024Article
- A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation.Journal of personalized medicine · 2024Article
- Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.BMC primary care · 2024Article
- [Digital competence in rhythmology : Training and education].Herzschrittmachertherapie & Elektrophysiologie · 2024Review
- Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference.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 · 2024Article
- Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis.Journal of diabetes science and technology · 2024Article
- Accuracy of continuous photoplethysmography-based 1 min mean heart rate assessment during atrial fibrillation.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 · 2023Article
- New concepts in atrial fibrillation pathophysiology.Herzschrittmachertherapie & Elektrophysiologie · 2022Review
- Editorial: Interplay between the heart and the immune system: Focus on heart rhythm regulation.Frontiers in physiology · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.
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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.