Diagnostic Accuracy of Artificial Intelligence for Arrhythmia Detection Using the 12-Lead Electrocardiogram: A Systematic Review and Meta-Analysis

Abstract Background Artificial intelligence (AI) has emerged as a promising tool for interpreting 12-lead electrocardiograms (ECGs), with the potential to enhance diagnostic accuracy for arrhythmia detection. However, published studies vary widely in methodology and validation strategy, warranting a quantitative synthesis of diagnostic performance. Methods A systematic review and meta-analysis was conducted according to the PRISMA-DTA 2018 guidelines and registered in PROSPERO (CRD420251027264). Searches were performed in MEDLINE, Embase, and Cochrane Library through September 2025 without language restrictions. Studies evaluating AI algorithms for arrhythmia detection using 12-lead ECGs were included. Data on sensitivity, specificity, and area under the curve (AUC) were extracted. Pooled estimates were generated using a bivariate random-effects model. Risk of bias was assessed with QUADAS-2, and the certainty of evidence was quantified using GRADE. Results 20 studies were included in the meta-analysis, encompassing over 5.5 million ECGs. The pooled sensitivity, specificity, and AUC for AI-based arrhythmia detection were 94.0% (95% CI 90.8–96.2; I² = 96.9%), 98.7% (95% CI 97.3–99.3; I² = 98.3%), and 0.982 (95% CI 0.965–0.986), respectively. Detection of atrial fibrillation (AF) yielded a sensitivity of 92.6% (95% CI 86.4–96), a specificity of 99.1% (95% CI 98.4–99.5), and an AUC of 0.988. Convolutional neural networks (CNN) specifically demonstrated a sensitivity of 97.6%, specificity of 98.7%, and an AUC of 0.982 for overall arrhythmia detection. When limited to external validation (n=6), the sensitivity was 96.9% (95% CI 89.2–99.1), specificity was 95.6% (95% CI 77.6–99.3), and AUC was 0.983. No significant publication bias was detected, and the overall certainty of evidence was rated as high. Conclusions AI models applied to 12-lead ECGs demonstrate excellent diagnostic performance for arrhythmia detection. Findings support potential integration into clinical workflows, particularly in settings with limited cardiology expertise. Given substantial heterogeneity, standardized datasets and multicenter prospective validation are essential to ensure effective and equitable implementation. What is Known Artificial intelligence has been increasingly applied to 12-lead electrocardiograms for arrhythmia detection, with multiple studies reporting high diagnostic accuracy. What the Study Adds This meta-analysis demonstrates consistently high diagnostic performance of artificial intelligence for arrhythmia detection on 12-lead ECGs, including atrial fibrillation and externally validated models. The substantial heterogeneity observed underscores the need for standardized datasets and multicenter prospective validation before widespread clinical implementation.