Artificial intelligence models excel in detecting and categorizing cardiac arrhythmias via ECG analysis, though their effectiveness relies heavily on large labeled datasets and high data quality.
AI-driven ECG analysis holds promise for revolutionizing cardiovascular care by enabling faster, more accurate diagnostics and personalized medicine.
Cardiovascular diseases (CVDs) are a global health concern, demanding advanced healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis is complex. Artificial Intelligence (AI) offers potential in improving diagnostic accuracy and uncovering new associations between ECG patterns and heart health risks. This paper reviews AI's historical evolution in CVD diagnosis, focusing on recent ECG analysis advancements and discussing societal implications and future research directions. AI has transformed medical decision-making, progressing from rule-based systems to modern machine learning (ML) and deep learning (DL) methods. By utilizing extensive datasets and advanced neural networks, AI models excel in detecting and categorizing cardiac arrhythmias. However, AI's effectiveness depends on access to large labeled datasets and collaboration within the biomedical community. AI-driven ECG analysis holds promise for revolutionizing cardiovascular care, enabling faster, more accurate diagnostics, and personalized medicine. Key challenges in cardiac arrhythmia classification with AI encompass data quality, class imbalance, and seamless integration with clinical workflows. Addressing these challenges is imperative for realizing the full potential of AI in cardiac care and ensuring accurate diagnosis.
Rahul et al. (Sun,) conducted a review in Cardiac arrhythmia. Artificial Intelligence (AI) for ECG analysis was evaluated. Artificial intelligence models excel in detecting and categorizing cardiac arrhythmias via ECG analysis, though their effectiveness relies heavily on large labeled datasets and high data quality.