Investigators developed AI software capable of detecting paroxysmal atrial fibrillation (PAF) from ECG recordings taken during sinus rhythm, addressing a diagnostic blind spot where intermittent arrhythmia eludes capture. The study demonstrated the model’s ability to infer prior or latent PAF signatures from standard ECG traces, potentially enabling earlier identification and treatment. Researchers validated the algorithm across clinical ECG datasets and reported strong sensitivity for PAF prediction, suggesting utility for screening and triage in ambulatory settings. Wider adoption would require prospective clinical trials to confirm impact on stroke prevention, anticoagulation decisions, and health economics before routine deployment.