Quantum-Enhanced Federated Learning with Explainable Multimodal Intelligence for Heart Disease Risk Stratification, Progression Analysis, and Personalized Care

Abstract

Cardiovascular disease is one of the greatest health challenges for people around the world, and there is an urgent need for new paradigms for data-driven prediction, risk stratification, and individualized cardiovascular care. Traditional diagnostic models rely on known risk factors like varying degrees, reflect the interrelated effects and contributions of genetic, lifestyle, clinical, and environmental factors, and raise questions about data privacy and reproducibility. This study outlines the potential for quantum-enhanced federated Learning (FL) and explainable multimodal intelligence (XMI) to work synergistically as an innovative solution for cardiovascular care. FL enables joint learning from multiple institutions in a networked way without sharing any of the individual patient-level raw data, and quantum computing has the potential to scale for multimodal health data. XMI provides transparency and establishes trust and interpretability for clinical decision making. A systematic review of selected examples indicate how FL and XMI can work together to improve cardiovascular care through the early diagnosis of cardiovascular disease, improved monitoring of disease progression, and the personalization of interventions. This paper highlights exciting opportunities to contribute to advancing precision cardiology through ethical, secure, and scalable AI-enhanced approaches.

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