Combining artificial intelligence (AI) with a standard electrocardiogram (ECG) acquired during normal sinus rhythm enables the point-of-care identification of individuals with atrial fibrillation (AF). Researchers have developed and validated an AI-enabled ECG that uses a trained neural network to detect the ECG signature of AF in a standard 10-s, 12-lead ECG recorded during sinus rhythm. A single AI-enabled ECG identified AF with an area under the curve of 0.87 (95% CI 0.86–0.88), a sensitivity of 79.0% (95% CI 77.5–80.4%), a specificity of 79.5% (95% CI 79.0–79.9%), an F1 score of 39.2% (95% CI 38.1–40.3%) and an overall accuracy of 79.4% (95% CI 79.0–79.9%). Performance improved with the inclusion of all ECGs acquired during the first month of each patient’s window of interest (from the start of the study for those without AF and from 31 days before the first recorded AF ECG for patients with AF).