Investigating the Efficacy of AI-Powered Innovations in ECG Analysis and Continuous Heart Monitoring: A Comprehensive Narrative Review.

Cardiovascular diseases (CVD) continue to be the primary global reason for death, while arrhythmias and other structural heart diseases create major diagnostic obstacles. The combination of artificial intelligence (AI) with electrocardiogram (ECG) analysis and continuous cardiac monitoring provides transformative capabilities for detecting diseases early and delivering individualized care and better patient results. The current study reviews the effectiveness, obstacles, and potential future directions of AI-based innovations in cardiovascular healthcare. The research used a complete literature review, which extracted information from PubMed/MEDLINE and Google Scholar and Scopus databases. The selection process for studies included only English-language research that explored AI applications in cardiovascular diagnostics. The search used "AI in ECG analysis" and "predictive analytics in heart monitoring" terms to identify relevant studies, which underwent qualitative analysis for extracting essential themes and findings. The analysis examined 11 research manuscripts that demonstrated how AI technology improves ECG interpretation by detecting subclinical patterns with high sensitivity and specificity to diagnose arrhythmias early. The combination of continuous cardiac monitoring with AI predictive analytics proved effective in detecting early clinical deterioration while minimizing diagnostic inconsistencies. The implementation of AI solutions faces ongoing challenges because of data privacy issues and device compatibility problems, and the requirement for real-world testing. AI-based advancements in cardiovascular diagnostics show great potential to transform clinical practice through individualized, precise medical interventions. These innovations face ongoing obstacles, but they create a fundamental advancement toward better cardiovascular health management. The full realization of AI's potential for patient care transformation depends on addressing the identified limitations.