COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE (AI) AND HUMAN EXPERTISE IN HEART RHYTHM DIAGNOSIS: A SYSTEMATIC REVIEW AND META-ANALYSIS

Abstract

Arrhythmias, characterized by irregular, fast, or slow heartbeats, can lead to severe complications if not detected and managed promptly. Artificial intelligence (AI) has emerged as a promising tool for analysing cardiac rhythm recordings, potentially improving the accuracy and efficiency of arrhythmia diagnosis. This systematic review and meta-analysis aimed to compare the accuracy of AI and human analysis in interpreting cardiac rhythm recordings and to explore the potential of AI to enhance diagnoses in pre-hospital care settings. A comprehensive search was conducted in multiple electronic databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and the Cochrane Library, to identify studies comparing the accuracy of AI and human analysis in interpreting cardiac rhythm recordings. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A random-effects model was used for meta-analysis, and subgroup analyses were performed based on AI algorithm type and data acquisition method. Twenty-two studies were included in the qualitative synthesis, and 18 were suitable for meta-analysis. The pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) were consistently higher for AI compared to human analysis. Deep learning algorithms demonstrated superior accuracy compared to machine learning algorithms. Studies using electrocardiogram (ECG) as the data acquisition method showed higher pooled AUC-ROC compared to those using Holter monitors. The findings suggested that AI algorithms, particularly deep learning methods, have higher accuracy in interpreting cardiac rhythm recordings compared to human analysis. AI-based diagnostic tools have the potential to improve the early detection and management of arrhythmias in pre-hospital care settings. However, further research is needed to validate these results in real-world clinical settings, address the limitations of current studies, and explore the long-term impact of AI on patient outcomes and healthcare delivery.