ReviewCurrent atherosclerosis reports2025
Machine Learning and Arrhythmia: Advances in Atrial Fibrillation Detection and Management.
Review in Current atherosclerosis reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewIn this paper we review recent advancements in the diagnosis and management of atrial fibrillation through machine learning (ML). RECENT
findingsMachine learning models developed from clinical records, electrocardiograms (ECGs) as well as data from implantable and wearable devices can now detect and even predict new-onset atrial fibrillation. Other models have improved prediction of stroke risk, increased the success of electrical cardioversions and facilitated catheter ablation of AF. Machine learning presents exciting new opportunities to enhance detection and management of atrial fibrillation. However, these developments need to be weighed against considerations of generalizability, equity, and transparency of these models for real-world utilization in clinical practice. We suggest targeted approaches for evaluation and utilization of ML models to allow for informed clinical implementation.
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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.