Integrating AI-Powered Multimodal Data for Early Cardiovascular Disease Detection and Personalized Predictive Healthcare

Abstract

Cardiovascular diseases (CVDs) are a significant global health issue that requires new ways for early detection, prevention, and treatment. Traditional care methods often struggle because healthcare data is fragmented and unstructured, which limits thorough risk assessments for individuals and populations at large. This chapter explores how artificial intelligence (AI) can bring together various data sources, such as electronic health records (EHRs), cardiac imaging, wearable sensors, and genomics, to fill these gaps. AI models, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can spot early or hidden signs of CVD, even in people who show no symptoms. AI-enabled models can help detect arrhythmias, predict acute events, and improve cardiac imaging. This chapter uses real-world case studies to show how AI-driven predictive care can improve outcomes and minimize the diagnostic delays. It also discusses challenges such as data privacy, algorithmic bias, and clinical integration. Subsequently, the focus will be on using AI in real time, exploring federated learning, and developing synthetic data innovations to create a stronger and more sustainable healthcare system.

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