Artificial intelligence-driven models consistently outperform traditional risk scores in cardiovascular risk prediction by integrating large-scale, multidimensional, and longitudinal data.
Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, emphasizing the ongoing need for effective and scalable primary and secondary prevention strategies. In this evolving landscape, artificial intelligence (AI) has emerged as a transformative force in preventive cardiology, with the potential to reshape risk assessment, early disease detection, and personalized preventive care. Artificial intelligence-driven models consistently outperform traditional risk scores by integrating large-scale, multidimensional, and longitudinal data derived from various platforms. These capabilities enable dynamic and time-adaptive cardiovascular risk prediction that more accurately reflects the evolving nature of individual risk profiles. Advances in machine learning and deep learning have facilitated the earlier identification of subclinical CVD often preceding clinical manifestation by several years. In parallel, AI-powered wearable devices and digital health (DH) solutions support continuous physi-ological monitoring, real-time feedback, and personalized lifestyle and behavioral interventions, thereby extending preventive care beyond traditional clinic-based settings. Such approaches appear particularly beneficial for high-risk populations by promoting sustained engagement, early intervention, and improved clinical outcomes. Looking ahead, emerging innovations such as multimodal AI systems, digital twin technologies, and AI-guided clinical guidelines signal a paradigm shift toward predictive, participatory, precision-based, and continuously learning prevention strategies. Nevertheless, the successful translation of AI into routine clinical practice will depend on increasing DH literacy, rigorous prospective validation, ethical and regulatory oversight, data transparency, and seamless integration into clinical workflows. When thoughtfully implemented, AI holds the promise to fundamentally advance preventive cardiology, enabling more patient-centered, participatory, and equitable cardiovascular care while reducing the global burden of CVD.
Keser et al. (Wed,) conducted a review in Cardiovascular diseases. Artificial intelligence vs. Traditional risk scores was evaluated. Artificial intelligence-driven models consistently outperform traditional risk scores in cardiovascular risk prediction by integrating large-scale, multidimensional, and longitudinal data.