Key result
AI shows potential to enhance arrhythmia detection and pacemaker analysis, though validation challenges limit implementation.
Why the study?
Driven by advancements in computational power and algorithmic innovations, AI and ML have demonstrated significant potential in cardiovascular medicine, motivating an evaluation of their applications in arrhythmia detection and pacemaker data analytics.
Systematic Review
Artificial intelligence and machine learning offer transformative capabilities for arrhythmia detection and pacemaker data utilization, but require rigorous prospective validation and standardized data frameworks to overcome current clinical implementation barriers.
Validation challenges limit routine clinical use of AI for arrhythmia detection; leaves open needs for external validation and standardization.
Driven by rapid advancements in computational power and algorithmic innovations, artificial intelligence (AI) and machine learning (ML) technologies have demonstrated significant potential in the field of cardiovascular medicine. This systematic review evaluates the applications of AI/ML in two key areas: arrhythmia detection and pacemaker data analytics. It examines the evolution of these technologies from traditional ML to deep learning, assessing their effectiveness in enhancing diagnostic accuracy, predicting clinical outcomes, and optimizing treatment strategies. Notably, this review supplements the practical application status of AI-augmented algorithms in mainstream clinical electrocardiogram (ECG) reading systems (represented by GE and Siemens platforms) in real-world clinical scenarios and systematically summarizes the latest research progress of AI-based intelligent analysis targeting pacemaker spike signals, which are easily ignored by conventional detection methods. The article also addresses current challenges, including technical limitations, barriers to clinical implementation, and ethical and regulatory considerations. Although issues such as data standardization, model interpretability, and external validation remain, AI/ML technologies already show transformative potential in arrhythmia management and pacemaker data utilization and are expected to play an increasingly central role in the future of personalized cardiology.
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Shen et al. (2026) conducted a systematic review in Cardiac arrhythmias and pacemaker data analysis. Artificial Intelligence and Machine Learning vs. Conventional detection methods was evaluated. Artificial intelligence and machine learning technologies demonstrate significant potential in enhancing diagnostic accuracy for arrhythmia detection and optimizing pacemaker data analysis, though clinical implementation remains limited by validation challenges.