The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation

Abstract

The integration of artificial intelligence (AI) into medicine offers transformative potential, particularly in the detection and management of atrial fibrillation (AF). However, the intersection of AI and AF has not been comprehensively evaluated. This systematic review focuses specifically on the applications of AI in AF risk prediction, monitoring, and management. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted in PubMed and Google Scholar using terms such as artificial intelligence, deep learning, machine learning, artificial neural networks, and AF diagnosis. Methodological quality and risk of bias were assessed using the JBI critical appraisal checklist for qualitative research. Of the 109 studies screened, 39 met the inclusion criteria. Of these, 19 studies focused on AI's role in AF risk prediction, while 20 studies addressed its application in monitoring and management. Machine learning models, including AI-ECG approaches such as the optimal time-varying machine learning model and the observational medical outcomes partnership common data model, demonstrated superior sensitivity and specificity compared to traditional models (Framingham, atherosclerosis risk in communities (ARIC), congestive heart failure, hypertension, age ≥75, diabetes, stroke, vascular disease (CHADS-VASc), and cohorts for heart and aging research in genomic epidemiology model for atrial fibrillation (CHARGE-AF). Wearable devices, such as patch monitors and smartwatches, emerged as reliable, cost-effective, and noninvasive alternatives to implantable cardiac monitors for continuous AF detection and patient-centered management. Despite these advances, the reliability and consistency of AI-based tools remain variable across studies due to data heterogeneity and methodological inconsistencies. Identified gaps include the need for standardized, labeled datasets, robust validation through prospective clinical trials, and improved data governance frameworks to ensure reliability and reproducibility. In conclusion, AI holds immense potential for AF prediction and management, but addressing these challenges is essential for its integration into clinical practice.

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