Personalized Medicine for Cardiovascular Disease Risk in Artificial Intelligence Framework

Abstract

Abstract Background & Motivation: The field of personalized medicine endeavors to transform the healthcare industry by advancing individualized strategies for diagnosis, treatment modalities, and prognostic assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Medical practitioners can use this strategy to tailor early interventions for each patient's explicit treatment or preventative requirements. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: In this comprehensive analysis, we conducted a detailed examination of the term "personalized medicine," delving into its fundamental principles, the obstacles it encounters as an emerging subject, and its potentially revolutionary implications in the domain of CVD. A total of 228 studies were selected using the PRISMA methodology. Findings and Conclusions : Herein, we provide a scoping review highlighting the role of AI, particularly DL, in personalized risk assessment for CVDs. It underscores the prospect for AI-driven personalized medicine to significantly improve the accuracy and efficiency of controlling CVD, revolutionizing patient outcomes. The article also presents examples from real-world case studies and outlines potential areas for future research.

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