Jul 2026· Artificial Intelligence and Applications· 0 citations· 27 references
TL;DR
The results indicate that the suggested framework can offer a scalable and privacy-aware security architecture for next-generation smart healthcare systems and must be treated as a simulation-based reference that needs to be validated against real healthcare datasets and deployment scenarios.
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
: Smart healthcare systems integrate the Internet of Things (IoT) and blockchain technologies, cloud infrastructures and advanced data analytic models to enable continuous monitoring, predictive diagnosis and intelligent decision making. However, the distributed nature of these systems, together with blockchain-based infrastructures and third-party processing, introduces significant privacy and security challenges. In parallel, the emergence of quantum computing threatens the long-term security of conventional cryptographic mechanisms. This paper examines the security vulnerabilities and threats associated with AI-based healthcare systems. It analyses privacy-preserving techniques for protecting data during processing as well as post-quantum cryptographic algorithms for secure communication and authentication. Finally, key challenges and future directions are discussed, highlighting the potential of combining homomorphic encryption and post-quantum cryptography for developing secure, resilient and hardware-efficient healthcare systems.
Kyriaki Tsantikidou, Nicolas Sklavos· Proceedings of the Internati...· 0 citations
The rapid digitalization of healthcare has led to the generation of vast amounts of sensitive patient information, increasing the need for advanced security solutions beyond traditional centralized systems. This study examines the integration of Artificial Intelligence (AI) and blockchain technology as a transformative approach to healthcare data security. Conventional electronic health record systems often face challenges such as single points of failure, limited transparency, and vulnerability to cyber threats. Blockchain addresses these issues by providing a decentralized and immutable ledger that ensures data integrity, traceability, and secure record management through cryptographic techniques and consensus protocols.
In parallel, AI strengthens security by enabling intelligent threat detection, predictive analytics, and adaptive authentication mechanisms. Machine learning algorithms continuously analyze network activities and user behaviors to identify potential breaches and insider threats in real time. The combination of AI and blockchain creates a synergistic framework in which AI enhances blockchain efficiency, while blockchain provides a transparent and trustworthy environment for AI-driven data processing.
The study further explores the role of blockchain-secured federated learning, which enables collaborative model training across healthcare institutions without exposing sensitive patient data. Key challenges, including interoperability, scalability, regulatory compliance, and integration with legacy systems, are also discussed. Additionally, patient empowerment is enhanced through self-sovereign identity models that grant individuals greater control over their personal health information.
Despite challenges related to computational complexity and standardization, the convergence of AI and blockchain offers a proactive, resilient, and privacy-preserving security architecture for modern healthcare. Future research should focus on lightweight cryptographic solutions, quantum-resistant security mechanisms, and governance frameworks for decentralized healthcare ecosystems. Overall, this integration represents a significant step toward secure, transparent, and patient-centered digital healthcare systems.
O. Gbolade· Journal of Information Techn...· 0 citations
The rapid digitalisation of healthcare has accelerated the adoption of telemedicine, Electronic Health Records (EHRs), and the Internet of Medical Things (IoMT), transforming healthcare delivery into a highly interconnected and patient-centric ecosystem. In response to growing concerns about data security, privacy, and interoperability, blockchain technology has emerged as a promising solution for its decentralization, immutability, auditability, and secure access control. However, many existing blockchain infrastructures rely on classical cryptographic primitives, including RSA- or elliptic-curve-based public-key mechanisms and cryptographic hash functions such as SHA-256, whose relevant security properties may be affected by sufficiently powerful quantum attacks. This review investigates the convergence of blockchain and quantum technologies to address emerging security threats in e-health systems. A structured literature review was conducted in accordance with the PRISMA 2020 guidelines using the IEEE Xplore, PubMed, ACM Digital Library, Google Scholar, and Crossref databases, covering studies published between January 2018 and June 2025. Following a systematic screening and eligibility-verification process, 57 relevant studies were selected and analyzed. The review evaluates quantum-resilient security mechanisms, including Quantum Key Distribution (QKD), Quantum Random Number Generation (QRNG), and NIST-standardized Post-Quantum Cryptography (PQC) algorithms specified in FIPS 203, FIPS 204, and FIPS 205. Based on the identified research gaps in the state of the art, this study also proposes a novel four-layer Quantum-Blockchain Security Architecture (QBSA) designed for secure healthcare environments. The analysis further reveals significant challenges associated with lightweight PQC deployment for IoMT devices, interoperability standardization, quantum hardware limitations, and regulatory compliance in cross-institutional healthcare systems. The findings highlight the necessity of integrating quantum-resilient cryptographic frameworks with blockchain infrastructures to support the development of secure, scalable, and patient-centric next-generation e-health ecosystems.
A scalable and adaptable paradigm for implementing fine-grained authorization in distributed systems, such as smart healthcare, is Attribute-Based Access Control (ABAC). ABAC offers dynamic data sharing capabilities, which are crucial for modern healthcare systems, by allowing access decisions based on user attributes. ABAC is frequently combined with cryptographic techniques to further improve the security of data transfer and protect private medical records in untrusted settings. However, it is still difficult to ensure both effective management of massive amounts of medical data and robust security against new quantum threats. In this paper, we present a hybrid quantum-safe ABAC framework for secure smart healthcare data sharing. The proposed technique enables secure and efficient access control over encrypted medical records by combining fine-grained attribute-based policy enforcement with lightweight cryptographic primitives. The proposed scheme is appropriate for practical smart healthcare settings, as it supports efficient access verification and dynamic attribute management. Security analysis shows that the framework maintains data secrecy and access control correctness while offering resistance against quantum adversaries. Performance evaluation shows that the proposed framework achieves lower computational, communication, and storage overhead compared to existing approaches. Thus, the proposed framework integrates fine-grained attribute-based access control with quantum-safe communication techniques and blockchain-assisted attribute management in order to enable secure, efficient, and scalable data sharing in smart healthcare systems.
The proposed framework effectively integrates encryption, federated intrusion detection, explainable artificial intelligence, and blockchain security to enhance privacy, transparency, and reliability in IoMT healthcare networks.
P. Banupriya, K. Vanitha· Journal of Vibration Enginee...· 0 citations
The rapid digitization of healthcare system operations and the widespread adoption of Internet of Medical Things (IoMT) devices has created an explosion in the amount of sensitive medical data, which has made them a target for cyberattacks. Thus, providing secure transmission and storage of Electronic Health Records (EHRs) is one of the most significant challenges facing the modern healthcare infrastructure. To address this issue, this paper presents a Hybrid Artificial Intelligence based Post-Quantum Encryption System (HAPES) framework for secure encryption and decryption of medical data. This proposed framework contains many types of Multi-Layered Encryption Algorithms, including AES-256 for data confidentiality, Elliptic Curve Cryptography (ECC) for efficient key exchange and Post-Quantum Cryptographic (PQC) Algorithms to ensure future security. Additionally, there will be an Artificial Intelligence based Anomaly Detection Module to identify potential cyber threats in real-time, and a Blockchain Layer to provide Tamper-Proof Data Integrity and Secure Audit Trails. Also included in HAPES is the Zero-Trust Security Model to provide Continuous Authentication and Authorization of Users. Experimental results show an improvement in Security Strength, Encryption Time, and Resistance to Cyber Threats compared with traditional encryption technologies. In summary, HAPES provides a scalable and efficient means to secure healthcare data in the Cloud and IoMT environments.
T. Prabhu, J. A. Kumar, R. M. Anushia et al.· International Conference on...· 0 citations