Blockchain-Enhanced Clinical Risk Management for AI Healthcare Systems : Privacy-Preserving Incident Response and Compliance Monitoring

Abstract

The rapid integration of artificial intelligence (AI) into healthcare systems has revolutionized clinical workflows, diagnostics, and decision support mechanisms. However, this transformation introduces significant challenges in clinical risk management, particularly concerning data privacy, incident traceability, and regulatory compliance. Traditional risk management frameworks struggle to provide tamper-resistant audit trails and privacy-preserving incident response mechanisms necessary for AI-driven healthcare environments. This study explores the application of blockchain technology as a foundational infrastructure for enhancing clinical risk management in AI healthcare systems. By leveraging blockchain's inherent properties of immutability, distributed trust, and cryptographic security, we propose a comprehensive framework that enables privacy-preserving incident response and continuous compliance monitoring. The research examines how blockchain-based solutions address critical gaps in algorithmic accountability, clinical incident documentation, and regulatory auditability while maintaining patient data confidentiality. Our findings demonstrate that blockchain technology can significantly improve transparency, traceability, and trust in AI healthcare systems, thereby supporting patient safety and regulatory adherence. This work contributes to the emerging intersection of blockchain technology, artificial intelligence, and healthcare risk management, providing practical insights for hospitals, AI developers, regulatory bodies, and clinical risk managers.

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