Role of Artificial Intelligence in Cardiovascular Risk Prediction and Prevention

Abstract

Globally, cardiovascular diseases (CVDs) continue to be the leading cause of death, making precise risk assessment and efficacious preventative measures imperative. Although essential, traditional cardiovascular risk assessment instruments like the Framingham Risk Score have shortcomings when it comes to precisely identifying individual risks. The use of Artificial Intelligence (AI) into the prediction of cardiovascular risk presents a revolutionary strategy to overcome these constraints. Artificial Intelligence (AI), which includes deep neural networks and machine learning algorithms, improves risk assessment through the analysis of large datasets, allowing for personalised risk forecasts that go beyond traditional risk indicators. The transition from population-based risk assessment to individualised profile is signalled by this integration, which will increase accuracy and facilitate prompt actions.

AI-powered models outperform conventional approaches in detecting complex risk variables and trends, providing higher forecasting accuracy. These models provide personalised risk profiles by utilising a variety of data sources, such as lifestyle, medical imaging, and genetic information. This allows for more focused preventative actions. In addition, AI applications in preventive cardiology include risk assessment, customised care plans, and early diagnosis via sophisticated imaging analysis.

Widespread adoption is hampered, nevertheless, by issues with data quality, AI model interpretability, generalizability across different populations, and ethical issues. In order to fully utilise AI to transform preventive cardiology and emphasise openness, morality, and ongoing technological breakthroughs, it will be essential to overcome these obstacles.