Key result
AI-enabled ECG interpretation boosts AF detection to ~98% sensitivity vs native Apple Watch algorithms.
Why the study?
Conventional wearable ECG validation excludes inconclusive results and assumes single-attempt testing, which inflates reported diagnostic performance for AF detection and fails to reflect real-world clinical use.
Does an intention-to-diagnose framework and AI-enhanced interpretation improve the realistic assessment and diagnostic accuracy of Apple Watch ECG recordings for atrial fibrillation detection in hospital participants?
Observational (n=296)
No
Does an intention-to-diagnose framework and AI-enhanced interpretation improve the realistic assessment and diagnostic accuracy of Apple Watch ECG recordings for atrial fibrillation detection in hospital participants?
Adopting an intention-to-diagnose framework that accounts for repeat testing and inconclusive results provides a more realistic assessment of wearable ECGs, while AI interpretation substantially reduces inconclusive outputs and improves diagnostic accuracy for atrial fibrillation.
AI ECG may outperform wearables in real-world testing; challenges conventional validation excluding inconclusives and leaves open prospective trials.
BACKGROUND Conventional wearable ECG validation excludes inconclusive results and assumes single-attempt testing, which inflates reported diagnostic performance for atrial fibrillation (AF) detection. Current reporting frameworks do not reflect real-world clinical use. OBJECTIVE To introduce and evaluate an intention-to-diagnose framework incorporating inconclusive outputs and repeat testing for realistic assessment of wearable ECG diagnostic performance in AF detection. METHODS Prospective observational study comparing three reporting frameworks-naive (exclude inconclusive), pragmatic (count as incorrect), and intention-to-diagnose (permit three attempts), applied to identical Apple Watch ECG recordings using native algorithm and AI-enabled neural network. Study conducted at a teaching hospital in Ireland with 296 participants after exclusions. RESULTS Apple Watch naive sensitivity/specificity were 96.1%/97.9%; pragmatic 78.1%/81.0%; intention-to-diagnose 92.2%/91.0%. The AI algorithm achieved pragmatic 98.4%/96.1% and intention-to-diagnose 98.4%/96.6%, with 92% reduction in inconclusive outputs versus Apple Watch. Repeatability was substantial for Apple Watch (kappa 0.77) and near-perfect for AI (0.96). CONCLUSION Wearable ECG reporting should adopt intention-to-diagnose frameworks, explicitly handling inconclusive outputs and repeat testing. Naive reporting inflates performance, while pragmatic reporting deflates it. AI-enhanced interpretation materially reduces inconclusive results and improves intention-to-diagnose accuracy, providing a more usable and reliable pathway for real-world AF detection.
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Doggart et al. (2025) conducted an observational in Atrial fibrillation (n=296). AI-enabled neural network ECG interpretation under an intention-to-diagnose framework vs. Native Apple Watch ECG algorithm across naive and pragmatic reporting frameworks was evaluated on Intention-to-diagnose sensitivity for atrial fibrillation detection. AI-enabled ECG interpretation achieved 98.4%/96.6% intention-to-diagnose sensitivity/specificity vs 92.2%/91.0% for Apple Watch, cutting inconclusive outputs by 92%.
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