Blockchain for Health Assistant Audit Trails and Consent Management: A Review of Implementations and Security Trade-offs

Abstract

Background: The rise of AI health assistants and digital tools raises concerns about data security and consent management. Traditional systems are prone to failures and provide limited transparency in data sharing. Blockchain technology offers a decentralized, immutable, and secure solution to these issues. Aim: This narrative review critically examines the real-world implementations and security trade-offs of blockchain technology when applied specifically to health assistant audit trails and consent management, moving beyond theoretical propositions. Methods: A systematic search of peer-reviewed literature (2010-2024) was conducted across Scopus, IEEE Xplore, PubMed, and ACM Digital Library. Implementation case studies, prototypes, and theoretical frameworks were analyzed to assess technical architectures, performance metrics, and security evaluations. Results: Findings indicate an emerging landscape where blockchain proves useful for creating secure audit logs in AI decision-making and dynamic consent models using smart contracts. However, challenges persist, including performance and scalability issues, key management complexities, data linkage risks, and conflicts between immutability and regulatory requirements such as the GDPR's "right to be forgotten." Conclusion: Blockchain serves as a foundational layer to improve security and transparency in health assistant ecosystems. Its future potential relies on hybrid architectures, advanced cryptographic methods such as zero-knowledge proofs, and an awareness of the new security and operational challenges that arise. It is not merely a database but a comprehensive solution for integrity and control.