Reimagining atrial fibrillation screening beyond age-based thresholds using AI

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.

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