Jul 2026· Journal of Information Technology, Cybersecurity, and Artificial Intelligence· 0 citations· 22 references
TL;DR
This study examines the integration of Artificial Intelligence (AI) and blockchain technology as a transformative approach to healthcare data security, and explores the role of blockchain-secured federated learning, which enables collaborative model training across healthcare institutions without exposing sensitive patient data.
Abstract
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.
This paper presents a blockchain-based trust architecture for adaptive healthcare AI comprising a four-layer system (user interaction, adaptive learning, blockchain governance, and IPFS storage), a version-controlled on-chain model registry, smart contract enforcement of update governance, and an explicit safety gate m...
Overall, this review demonstrates that blockchain-based cybersecurity frameworks provide a secure, transparent, and resilient foundation for protecting smart digital environments against increasingly sophisticated cyber threats while supporting trustworthy and scalable digital transformation.
M. Kayla, Crispinus Ode, Marion Sanaipei· The Eastasouth Journal of In...· 0 citations
The study evaluates the synergistic potential of AI-driven threat detection and Blockchain-enabled data integrity mechanisms, demonstrating that integrated AI-Blockchain frameworks achieve 94.3% reduction in unauthorized access attempts and 98.08% diagnostic accuracy in privacy-preserving analytics.
ABSTRACT
The increasing digitization of healthcare has amplified the need for secure, scalable, and interoperable systems capable of managing sensitive patient information. Traditional centralized healthcare data management systems are limited by issues of data silos, vulnerability to tampering, and single points of fa...
Dhruv Gupta, Sandeep Tiwari· International Scientific Jou...· 0 citations
This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their...
Harsh Verma· International Journal of Sci...· 0 citations
A novel blockchain based federated learning framework for non-sensitive cross-institutional medical data research which integrates decentralized blockchain networks with federated model aggregation offering secure parameter exchange, transparent participant validation, tamper-resistant audit trails and increased trust...
Narendra Karmarkar· International Journal of Eme...· 0 citations
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