Abstract 4371686: Impact of Artificial Intelligence on Cardiovascular Disease Diagnosis, Risk Assessment, and Treatment: A Meta-Analysis of 45 Studies

Abstract

Background: AI holds great potential in improving cardiovascular disease diagnosis, risk assessment, and treatment. However, its clinical utility requires thorough validation through randomized controlled trials and real-world evidence. This meta-analysis evaluates the impact of AI on cardiovascular outcomes across various study designs. Methods: A systematic search across PubMed, Scopus, and ClinicalTrials.gov identified 45 studies (15 RCTs, 30 observational) from January 1, 2015, to January 1, 2025, with over 50,000 patients. The studies focused on AI models, including machine learning, deep learning, and natural language processing, applied to diagnostic imaging (e.g., ECG, echocardiography), risk prediction, and personalized treatment in cardiovascular conditions like CAD, heart failure, and arrhythmias. Random-effects meta-analysis was used to calculate summary effect sizes, accounting for study heterogeneity. Results: Our analysis demonstrates that AI-driven models substantially improve diagnostic performance in high-prevalence conditions like CAD and HF. Specifically, AI tools augmented diagnostic accuracy by 12% overall, achieving a sensitivity of 88% (versus 75% for conventional methods) and a specificity of 91% (versus 84%) in real-world clinical workflows. In heart failure management, AI-powered risk stratification models were associated with a significant 15% reduction in 5-year all-cause mortality (hazard ratio: 0.72, 95% CI: 0.65–0.79, p < 0.01), reflecting their ability to identify high-risk patients for targeted interventions. Furthermore, AI algorithms predicting adverse cardiovascular events led to a notable 20% reduction in 30-day readmissions (relative risk reduction: 0.80, 95% CI: 0.75–0.85), as observed in large health system datasets. Beyond prediction, AI-based personalized treatment recommendations, derived from electronic health records and patient-specific physiological data, improved patient outcomes by 9%, particularly beneficial for elderly patients grappling with multi-morbidities. Crucially, real-world deployments of AI demonstrated a 17% reduction in unnecessary diagnostic procedures and a 12% decrease in time-to-diagnosis for acute events such as myocardial infarction, highlighting efficiency gains in routine clinical practice. Conclusions: AI shows promise in improving cardiovascular disease management, with better outcomes like reduced mortality and improved diagnostics.

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