Reimagining atrial fibrillation screening beyond age-based thresholds using AI.
Where this comes from
- Record sourced from PubMed, PMID 41888221.
- Also identified by DOI 10.1038/s41746-026-02485-w and PMC identifier 13187120.
- Licence recorded as CC BY-NC-ND.
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Abstract
Atrial fibrillation (AF) affects over 50 million people worldwide and carries substantial downstream morbidity, mortality, and cost. Yet many contemporary screening programs rely primarily on age thresholds—an approach that is operationally simple but can be imprecise for identifying near-term risk. AI applied to handheld single-lead ECGs can predict incident AF with accuracy similar to established clinical risk scores, but real-world deployment remains limited by signal noise, workflow complexity, and unclear risk thresholds.