Publication Details
Abstract
The use of artificial intelligence (AI) in cardiovascular disease (CVD) risk prediction has revolutionized preventive cardiology by improving diagnostic precision, early intervention, and health equity. The use of various datasets, including genomic, wearable, imaging, and electronic health records, is highlighted in this paper, which summarizes recent advancements in AI-based risk prediction models for CVD. The construction, creation, and validation of AI models are covered, with a focus on new predictors and how they affect model performance. The paper also examines the differences brought about by algorithmic bias, showing how underrepresentation of particular demographic groups can worsen health inequities and reduce predictive reliability. It is recognized that AI can perform better than conventional statistical models in some situations, especially when it comes to identifying at-risk persons and directing healthcare decisions. Nonetheless, there are still issues with the model's fairness, openness, and generalizability. In conclusion, even though AI has the potential to improve cardiovascular risk assessment and individualized treatment, thorough model evaluation and bias reduction techniques are essential to guaranteeing fair, dependable, and successful clinical application.