AI-ECG alone shows modest ability to identify atrial fibrillation risk.
AI-ECG Risk Stratification for Atrial Fibrillation: Real-World Performance and Explainability.
Kouki Matsuo et al. · JACC. Advances · 2026
Background
Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a potential method for identifying atrial fibrillation (AF) from sinus rhythm.
Purpose
The objective of the study was to assess the performance and explainability of an AI-integrated ECG system for AF risk stratification in a multicenter real-world cohort.
Methods
We enrolled 665 patients aged ≥40 years who underwent 12-lead ECGs using an AI-enabled electrocardiograph (FCP-9900).
n = 665 patients
0.64-0.69
Results
The modest ability of AI-ECG alone to predict irregular heartbeat risk
n = 665 patients
More results
AF prevalence increased across AI-ECG risk categories, with significantly higher odds in the mid-high and high groups vs low.
SHapley Additive exPlanations analysis showed CHA 2 DS 2 -VASc as the most influential predictor, whereas AI-ECG provided modest incremental value.
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Conclusion · 1 of 2
AI-ECG provides rapid, low-cost AF risk estimation from a single sinus rhythm ECG; however, its predictive performance is modest compared with clinical scores.
Conclusion · 2 of 2
At present, AI-ECG may complement, but not replace, traditional risk stratification.