Artificial Intelligence and Blockchain as Determinants of Healthcare Data Security Transformation

Blockchain-Enabled Trust for Adaptive Artificial Intelligence in Healthcare

: The deployment of adaptive artificial intelligence (AI) in healthcare, where models continue to learn or retrain following initial deployment, introduces governance challenges that are foundationally distinct from those of static AI. In regulated clinical environments, these challenges manifest as four interrelated failure modes: concept drift and silent performance degradation, update poisoning and data integrity compromise, governance circumvention through ungoverned incremental modifications, and the collapse of model versioning and post-hoc auditability. 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 mechanism that activates a predetermined change control plan at the infrastructure level. The architecture extends a completed blockchain trust framework for static AI models and is motivated by a public cybersecurity survey in which blockchain was identified as the most frequently selected technology for improving healthcare system security following a data breach. The system is evaluated using the Architecture Trade-off Analysis Method (ATAM), with particular reference to the quality attributes of auditability, safety, scalability, and regulatory compliance.

Blockchain-Driven Cybersecurity Framework for Smart Digital Ecosystems

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.

Cybersecurity, and Artificial

A Blockchain-Enabled Approach for the Secure Transfer of Healthcare Information Thesis

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 failure. Although blockchain technology has emerged as a promising solution, existing blockchain-based frameworks face challenges of scalability, latency, and adaptability to real-time Internet of Things (IoT)-driven healthcare data. To address these limitations, this study proposes MedChain, a blockchain-enabled framework designed to provide secure, scalable, and patient-centric healthcare data sharing. The research follows a systematic methodology. First, a conceptual framework was developed, integrating blockchain, digest chains, and a session-based data-sharing model to balance security with efficiency. A prototype system was implemented and tested using simulated healthcare datasets, including both static electronic health records (EHRs) and dynamic IoT-generated data streams. The evaluation involved comparative analysis against traditional centralized and blockchain-only systems, focusing on key performance metrics such as throughput, latency, scalability, and tamper detection. The results demonstrate that MedChain significantly outperforms existing models. Throughput improved by 87.5% over blockchain systems and 25% over centralized systems, while latency was reduced by 54.3% compared to blockchain. Tamper detection achieved a near-perfect accuracy of 99%, and scalability tests confirmed MedChain’s ability to sustain performance under large data loads (up to 50,000 records), where blockchain-only systems experienced severe slowdowns. These findings validate the effectiveness of the proposed framework in supporting secure and efficient healthcare data exchange. This study contributes to both theory and practice by introducing a layered architecture that enhances blockchain’s applicability in healthcare. It demonstrates how digest chains reduce verification overhead, while session-based sharing empowers patients with granular control over their data. Furthermore, the research highlights practical insights for healthcare providers and policymakers seeking to adopt blockchain-enabled solutions. In conclusion, MedChain provides a viable pathway toward secure, scalable, and patient-centered healthcare data management. While the results are promising, the study also identifies areas for future exploration, including energy-efficient consensus mechanisms, compliance with evolving healthcare regulations, and integration with large-scale IoT ecosystems. These directions will further refine the potential of blockchain to revolutionize healthcare data sharing.

Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications

Blockchain and artificial intelligence (AI) are converging into a single infrastructural layer for securing data sharing, model integrity, and autonomous decision-making across distributed systems. 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 lifecycle and situates their findings within the emerging literature on blockchain-enabled AI and autonomous AI agents. Each constituent study addresses a distinct point of failure in modern AI-driven security operations: the integrity of training data and model behavior, the reliability of real-time monitoring, and the trustworthiness of automated code remediation. We argue that blockchain's properties of immutability, decentralized consensus, and verifiable provenance directly address a gap common to all three: the difficulty of establishing trust in data, models, and autonomous agents that operate without a central authority. Building on real-world research on blockchain-secured data sharing, federated learning, and multi-agent coordination, we propose a layered reference architecture that couples adversarially hardened models, blockchain-anchored data provenance, AI-driven anomaly detection, and smart-contract-governed multi-agent remediation. We conclude by identifying open problems in scalability, privacy-transparency trade-offs, and the governance of autonomous agents that must be resolved before such integrated systems can be trusted in production-critical environments.

Blockchain-Integrated Federated Learning for Cross-Institutional Medical Research

The rapid digital transformation of healthcare has generated enormous volumes of heterogeneous medical data from hospitals, research laboratories, diagnostic centers, and wearable healthcare devices. These distributed datasets present unprecedented opportunities for developing intelligent clinical decision support systems using artificial intelligence (AI). However, strict privacy regulations, institutional policies, and cybersecurity concerns significantly restrict the sharing of sensitive patient information across organizations. Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables collaborative model training without exchanging raw medical data. While conventional FL offers inherent advantages, it is subjected to challenges including centralized aggregation and impacts from malicious participants such as participating adversarial agents performing model poisoning attacks or moving around in a vehicular network leading to non-transparent trust management. Decentralization consensus, immutability, traceability and tamper-proof transaction record capabilities of blockchain technology is an excellent complement to federated learning. This paper proposes a novel blockchain based federated learning framework for non-sensitive cross-institutional medical data research. We present a solution which integrates decentralized blockchain networks with federated model aggregation offering secure parameter exchange, transparent participant validation, tamper-resistant audit trails and increased trust between collaborating healthcare institutions. Smart contracts provide data security, model privacy and automated protocol of database access as well as a rudimentary sense of control to prevent forgery. The framework achieves substantial gains in privacy preservation, collaborative intelligence, system scalability, and adversarial attack robustness. This integration enables safe, reliable and scalable collaborative medical intelligence for efficient multi-institutional medical research, accelerating AI-based health innovations, and maintaining compliance with recent healthcare privacy regulations.