AI-based model for maintaining good healthcare quality against cybersecurity risks

Abstract

Artificial Intelligence (AI) has strong potential in health monitoring systems to support high-quality healthcare while mitigating cybersecurity risks. AI-based solutions for health and wellness applications, particularly for cardiovascular disease monitoring, are being explored to address complex healthcare challenges and improve patient outcomes. The integration of quantum and AI-based techniques is also gaining attention for enhancing future healthcare applications and communication technologies. Purpose: The primary objective is to improve cardiac care by accurately predicting symptoms and mitigating cyber-risks that threaten digital health integrity. By leveraging Integrated Quantum Networks (IQNs) and AI-driven protocols, this research aims to reduce the prevalence/incidence of non-communicable diseases by 50% by 2035 through proactive prevention and superior treatment management. Method: The framework utilizes AI-based techniques and AI-quantum-enhanced sensors and IQN to build a secure, proactive monitoring system. This theoretical framework integrates high-precision data collection with robust risk management systems to protect against vulnerabilities in digital health infrastructure. These components work in tandem to ensure that sensitive medical data remain resilient against emerging cyber threats. Anticipated Results and Conclusions: The system is expected to improve cybersecurity resilience, system performance, and energy efficiency (EE), supporting the development of secure and advanced future healthcare applications