The goal of this clinical trial is to learn whether an artificial intelligence-enhanced electrocardiogram (AI-ECG) strategy improves timely intervention of patients requiring cardiac implantable electronic devices (CIEDs), compared with standard clinical care.

This is a randomized controlled trial designed to evaluate the impact of an AI-ECG strategy on the identification of patients requiring CIEDs. The ECGs of eligible participants will be analyzed by a previously validated deep learning algorithm. Those classified as high-risk by the AI-ECG system will be allocated at random into either the intervention group or the control group.

In the intervention group, the physicians will be alerted by the AI-ECG system, and the participants will be proactively contacted to receive ambulatory continuous ECG monitoring for up to 7 days. In the control group, the participants will continue with usual clinical care, and treating physicians will not have access to the AI-ECG results before the end of this study. To ensure accuracy, the reference standards for device indications will be performed by a panel of experienced cardiologists without access to the AI-generated reports.

Inclusion Criteria:

• At least one 12-lead ECG within 1 year

Exclusion Criteria:

• Diagnosis of sick sinus syndrome
• Diagnosis of high-grade or complete atrioventricular block
• Diagnosis of ventricular tachycardia or ventricular fibrillation
• Post CIED implant
• Heart rate below 40 beats per minute by 12-lead ECG