The evolution of AI-powered precision medicine in the context of CVD prevention emerges as an interdisciplinary convergence of computer science, clinical medicine, and data analytics [2], [3]. Fundamentally, AI algorithms are now capable of processing extensive amounts of heterogenous data acquired from electronic health records, advanced imaging modalities, and wearable sensor devices, each contributing to a comprehensive risk profile [8], [9]. These advances resonate with the principles of precision medicine that prioritize individualized care by leveraging patient-specific information to optimize clinical decision-making [6], [5]. The contemporary literature highlights that the combination of high-throughput data acquisition and AI’s analytical capability opens new avenues for early detection and targeted intervention [1], [10]. Notably, the integration of explainable AI models has introduced transparency into the decision-making processes, thereby increasing the confidence of clinicians in adopting these novel diagnostic tools [11], [12].

Cardiovascular diseases remain the leading cause of death globally, prompting an urgent need for improved diagnostic and therapeutic methodologies [1], [13]. Traditional risk models based on clinical scoring systems have increasingly become insufficient in capturing the complexity and heterogeneity of CVDs [7], [14]. In contrast, AI-based approaches have demonstrated superior performance by detecting subtle patterns and biomarkers that often escape conventional detection methods [8], [15]. This shift towards predictive analytics is especially significant given that early and precise risk stratification can potentially mitigate the progression of cardiovascular events [2], [16]. As such, the current literature emphasizes the transition from generic treatment protocols to individualized management plans, accentuating the role of AI in realizing the full potential of precision cardiovascular medicine [17], [18].

The clinical applicability of AI in cardiovascular medicine is underpinned by its capacity to integrate vast clinical datasets into actionable insights [19], [17]. By harnessing deep learning and advanced ML algorithms, researchers have built models that predict the likelihood of cardiovascular incidents with high sensitivity and specificity [20], [21]. Moreover, these predictive models often incorporate real-world data, further enhancing their robustness and accuracy by reflecting the heterogeneous nature of patient populations [10], [22]. The proliferation of high-performance computing resources and the digitization of health records have accelerated the incorporation of AI tools into routine clinical practice [23], [24]. In summary, this transformative approach holds the promise of not only advancing diagnostic capabilities but also streamlining therapeutic interventions, thereby improving patient outcomes through precision-driven management [14], [25].

A significant component of precision medicine in cardiovascular care is the ability to personalize risk assessment and treatment strategies across diverse populations [2], [6]. The data amassed from multiple sources such as imaging, genomic sequencing, and wearable devices have laid the foundation for AI algorithms to delineate individual risk factors with exceptional specificity and granularity [26], [9]. Consequently, such technological advancements have spurred the development of targeted interventions designed to mitigate disease progression through early preventive measures [5], [13]. Clinically, these innovations facilitate a shift from reactive treatment modalities to proactive management of cardiovascular health, reflecting a critical advancement in public health strategy [1], [7]. Additionally, the iterative refinement of these AI-powered models through continuous learning and integration of new data is pivotal to sustaining their clinical relevance and accuracy [12], [17].

In recent systematic reviews and meta-analyses, precision medicine has been recognized as a transformative approach that addresses the inherent variability in patient responses to treatment [7], [17]. The ability to harness AI for aggregating and analyzing large-scale patient data is central to this progress, providing insights into disease mechanisms and therapeutic response [2], [3]. With continuous improvements in computing technology, sophisticated algorithms are now standard in translating clinical and multi-omics data into refined diagnostic tools [4], [12]. These advances represent a convergence point between computational innovations and clinical necessity, thereby reinforcing the relevance of AI-enabled precision medicine in cardiovascular disease management [8], [15]. The holistic synthesis of diverse data types through AI fosters a more accurate and individualized prediction model that emphasizes prevention over treatment [5], [13].