Clinical Trial Data Sharing Through Blockchain-AI Platforms

Abstract

Clinical trial data are essential public goods, yet sharing them remains constrained by fragmented infrastructure, inconsistent consent management, complex regulatory landscapes, and legitimate concerns about privacy, security, and scientific misuse. Traditional repositories and bilateral data-use agreements often struggle to provide verifiable provenance, enforceable permissions, and scalable mechanisms for cross-institutional analysis. This manuscript proposes and analyzes a reference architecture for a permissioned blockchain–AI platform that enables compliant, trustworthy, and efficient sharing and secondary use of clinical trial data. The platform couples a consortium ledger for tamper-evident consent, access control, and auditability with a privacy-preserving AI layer that supports federated learning, differential privacy, and secure aggregation across sites. Interoperability is addressed via HL7 FHIR-based schemas and metadata harmonization pipelines; off-chain encrypted stores (e.g., content-addressed storage) manage bulk data while on-chain smart contracts encode governance rules, data-use conditions, and revocation. We situate the approach within current policy frameworks (e.g., ICMJE data sharing, EU GDPR, NIH Data Management and Sharing Policy, EMA Policy 0070) and synthesize evidence from the healthcare blockchain and federated learning literatures regarding security, performance, and adoption barriers. A methodology section details system components, identity/consent models, threat assumptions, and evaluation metrics (governance, privacy, model utility, and operational performance). The results section presents a design-level analysis and an implementation blueprint derived from prior prototypes and standards, along with realistic operational scenarios—such as consent withdrawal mid-trial, cross-border data requests, and audit preparation for regulators. We conclude that blockchain–AI platforms can materially improve verifiability, accountability, and privacy-by-design for trial data sharing, provided that governance is robust, identity and key management are user-centric, and interoperability and change management are prioritized.