Artificial Intelligence–driven Cardiology Expert Systems: Integrating Bioelectric Signal Analysis With Advanced Diagnostic Frameworks

Artificial intelligence (AI) has revolutionized cardiovascular healthcare by enabling automated analysis of complex biomedical data and supporting evidence-based clinical decision-making. AI-driven cardiology expert systems integrate bioelectric signal analysis, particularly electrocardiogram (ECG) interpretation, with advanced diagnostic frameworks to improve the detection, classification, and management of cardiovascular diseases. These systems employ machine learning, deep learning, and knowledge-based reasoning techniques to process large volumes of cardiac data with high accuracy and efficiency. By combining real-time bioelectric signal monitoring with intelligent diagnostic algorithms, AI-powered platforms facilitate early disease detection, risk stratification, and personalized treatment planning. Furthermore, expert systems enhance healthcare accessibility by supporting telemedicine and remote patient monitoring applications. This paper explores the architecture, methodologies, and clinical significance of AI-driven cardiology expert systems, emphasizing the integration of bioelectric signal processing with advanced diagnostic frameworks. Challenges related to data quality, interpretability, regulatory compliance, and ethical considerations are also examined. The study highlights future directions for intelligent cardiovascular healthcare technologies.