Real world study of opportunistic supraventricular tachycardia detection from three million single lead AI ECGs

Supraventricular tachycardia (SVT) is a common paroxysmal arrhythmia whose intermittent nature limits detection by conventional electrocardiogram (ECG) monitoring. Mobile artificial intelligence (AI)–enabled ECG systems offer a scalable alternative, yet their clinical value and real-world effectiveness for SVT detection remain insufficiently evaluated. In this retrospective real-world study, we analyzed 3,566,626 single-lead ECG recordings from 84,242 users collected in China between August 2019 and May 2025. We assessed the system’s ability to identify SVT events, patterns of patient-initiated specialist review, clinical workflow efficiency, and the potential impact on patient time and costs. Here we show that the mobile system accurately identifies SVT events in real-world use (ROAUC 0.866; 95% CI, 0.863-0.869), with acceptable discrimination and reliable risk estimation (Brier score 0.123). Among recordings identified as high risk and subsequently reviewed by cardiologists, 96.30% are confirmed as SVT. The combination of automated analysis and patient-initiated specialist review reduces the number of recordings requiring cardiologist assessment per confirmed case by 98.8%. Compared with repeated hospital-based evaluations, this approach reduces patient costs by 79.1% and time burden by 95.7%. This study shows that an AI-ECG mobile system combined with patient-initiated specialist review provides an efficient approach for opportunistic detection of SVT in daily life. This strategy may complement clinician-led care by improving the efficiency of cardiac monitoring and triage. Supraventricular tachycardia (SVT) is a type of abnormal heart rhythm that can occur suddenly and may be difficult to detect because episodes are often brief and unpredictable. We investigated whether a portable single-lead electrocardiogram (ECG) device, a small device that records the electrical signals generated by the heart through electrodes placed in contact with the skin, combined with artificial intelligence could help identify SVT in everyday life. We analyzed more than 3.5 million ECG recordings from over 80,000 users in China and evaluated how the system worked together with patients and cardiologists. The system showed good ability to identify possible SVT events and helped cardiologists focus on recordings with a higher likelihood of abnormal rhythms. This approach may provide a more convenient and efficient way for people to monitor heart rhythm outside hospitals and support earlier medical attention when needed. Fan, Zhang et al. evaluate the health-system utility of an AI-enabled ECG mobile system for detection of supraventricular tachycardia using over 3.5 million recordings in China. The system supports real-world cardiac monitoring through AI-assisted triage and patient-initiated review, offering a framework for future digital health tools.