AI-driven Innovations in Cardiology: A Comprehensive Review of Effectiveness and Challenges

Background: Cardiovascular diseases (CVD) are the leading cause of mortality in China, accounting for 40% of annual deaths and affecting over 290 million individuals. Rapid urbanization, lifestyle changes, and an aging population have exacerbated CVD risk factors such as hypertension, diabetes, and obesity. Artificial Intelligence (AI) technologies, including machine learning (ML) and deep learning (DL), offer transformative potential to address these challenges by enhancing diagnostic accuracy, risk stratification, and patient management. Objectives: This systematic review evaluates the effectiveness of AI in improving CVD diagnosis and treatment outcomes within the Chinese healthcare system. Secondary aims include assessing AI’s role in risk prediction, identifying implementation barriers, and exploring future directions. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search (2021-2025) across PubMed, CNKI, IEEE Xplore, Scopus, and Web of Science. Inclusion criteria focused on peer-reviewed studies involving AI applications (ML/DL) in adult CVD care, while excluding non-empirical research or studies outside China. Results: Preliminary findings demonstrate that AI significantly enhances diagnostic precision (e.g., CNNs for ECG interpretation, DL for imaging analysis) and enables personalized treatment plans. Challenges include infrastructural limitations, data privacy concerns, and clinician resistance due to inadequate training. AI-driven predictive analytics show promise in early intervention but require robust validation and ethical oversight. Conclusion: AI holds immense potential to revolutionize CVD care in China, though its integration demands addressing technological, educational, and ethical barriers. Future research should prioritize longitudinal studies and standardized frameworks to ensure equitable, transparent AI deployment in cardiology.