Post-Quantum Blockchain and AI-Based Security Framework for Smart Healthcare Systems

Abstract

The security of smart healthcare systems has become an important research topic as patient records are progressively digitalized, and new cyber threats continue to emerge. Traditional cryptographic mechanisms may become less reliable in the presence of quantum computing capabilities, which motivates the need for quantum-resistant security solutions. A simulation-based human-in-the-loop reference architecture for smart healthcare systems with post-quantum cryptographic mechanisms, blockchain-enabled auditability, and artificial intelligence (AI)-enabled threat detection to enhance security, privacy, and real-time monitoring is proposed. Our proposed framework integrates post-quantum digital signatures, quantum-resistant key establishment mechanisms with tamper-evident blockchain logging, and AI-based anomaly detection to facilitate secure and traceable healthcare data processing. Simulation experiments were performed using synthetic healthcare cybersecurity scenarios involving electronic health record access behavior and Internet of Medical Things traffic (e.g., unauthorized access attempts, anomalous data exfiltration, SQL injection, or distributed denial-of-service (DDoS)-like flooding attacks). The proposed AI-based detection model achieved an accuracy of 92.1%, a precision of 91.4%, a recall of 93.2%, an F1-score of 92.3%, and a false-positive rate (FPR) of 5.1%, with a detection-cycle latency of 200 ms. Scalability was evaluated using up to 10,000 simulated healthcare endpoints, with stable detection performance and blockchain-logging behavior observed in the simulation environment. The results indicate that the suggested framework can offer a scalable and privacy-aware security architecture for next-generation smart healthcare systems. The framework must be treated as a simulation-based reference that needs to be validated against real healthcare datasets and deployment scenarios.   Received: 18 March 2026 | Revised: 26 May 2026 | Accepted: 22 June 2026   Conflicts of Interest The author declares that he has no conflicts of interest to this work.   Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.   Author Contribution Statement Ahmad H. Alenezi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – revie