Abstract Introduction A novel smartwatch (Withings Scanwatch, SW) offers automated analysis of the QTc based on single-lead ECG recordings. Prior clinical validation studies showed insufficient accuracy when compared to manual measurements based on a 12-lead ECG. Purpose We hypothesized that a novel AI-based algorithm (PulseAI) is capable of measuring QTc-interval based on PDF-exports from a single-lead ECG with improved accuracy compared to the manufacturer’s automated QTc measurements. Methods In this prospective, observational study, consecutive patients underwent a nearly simultaneously 12-lead ECG and a single-lead ECG. Two blinded, independent cardiologists manually interpreted the QT-interval in leads I and II of the 12-lead ECG using the tangent-method, which served as the gold standard. QTc was calculated using Bazett’s formula. QTc measurements were compared using the Bland-Altman method. Results A total of 317 patients (48% female, mean age 63 ± 17 years) were enrolled. QTc-intervals could be automatically calculated by the SW and by the novel AI algorithm in 175 patients (55%). Diagnostic accuracy of SW-ECG for detection of QTc-intervals ≥ 450 ms as quantified by the area under the curve was 0.85 for the manufacturer and 0.9 for novel AI algorithm. In 21 patients (12%) QTc was ≥450ms. The Bland-Altman analysis resulted in a bias of 21 ms [95% limit of agreement (LoA) -48 to 89 ms] for the manufacturer algorithm and 2 ms [95% LoA -35 to 39 ms] for the novel AI algorithm, respectively, when compared with manual QTc-measurement based on the 12-lead ECG. Conclusion A novel AI algorithm outperformed the manufacturer algorithm for automated QTc-measurements based on PDF-exported single-lead ECGs.Central Illustration