Researchers developed AI software that detects paroxysmal atrial fibrillation (PAF) from ECG recordings captured in sinus rhythm, addressing a key diagnostic blind spot. The tool analyzes subtle waveform features to flag patients at risk of intermittent AF episodes that standard monitoring can miss. The study reports performance metrics demonstrating improved sensitivity over traditional screening approaches in retrospective datasets. Authors emphasize the AI is intended as a triage or screening aid rather than a replacement for confirmatory rhythm monitoring. If prospectively validated, the algorithm could expand population screening strategies, inform remote monitoring device design, and influence reimbursement decisions around diagnostic AI in cardiology.