AI and Blockchain for Patient-Centric Genomic Data Governance: A Privacy-Preserving Healthcare Engineering Framework
Main article
Abstract
Genomic data have become central to precision medicine, risk stratification, pharmacogenomics, and population health research, yet their governance remains difficult because sequence data are sensitive, persistent, identifiable, and valuable across multiple future clinical contexts. Conventional electronic health record architectures protect data through institutional access control, but they often provide limited patient agency, weak cross-institutional provenance, and fragmented consent management. This paper develops a patient-centric healthcare engineering framework that integrates artificial intelligence and permissioned blockchain for privacy-preserving genomic data governance. The framework separates encrypted off-chain genomic storage from on-chain metadata, consent events, audit records, and smart-contract access rules. AI modules provide privacy-risk scoring, anomalous access detection, policy recommendation, and data-quality assessment, while blockchain components provide tamper-resistant logs, decentralized identity, consent execution, and verifiable provenance. Drawing on literature in healthcare blockchain, genomic privacy, trustworthy AI, federated learning, and interoperability standards, the paper proposes a layered architecture and conducts a scenario-based engineering evaluation comparing centralized EHR governance, blockchain-only governance, and an AI-blockchain hybrid. Results suggest that the hybrid model improves normalized governance scores for integrity, consent automation, privacy protection, audit completeness, and interoperability, while reducing avoidable access latency through AI triage and off-chain storage. The study contributes a deployable design logic for genomic data stewardship that balances patient autonomy, research utility, regulatory compliance, and engineering scalability.
