Clinical AI systems rely on evolving machine learning pipelines and large-scale medical imaging data, creating persistent challenges in trust, auditability, consent governance, and reproducibility. This paper proposes a decentralized governance framework for clinical AI that uses blockchain as a verification and policy-enforcement overlay without decentralizing sensitive medical data storage or clinical inference. Raw images and clinical artifacts remain in secure repositories, while cryptographic commitments, consent states, access events, and reproducibility manifests are anchored to a tamper-evident ledger. The framework enables verifiable provenance, programmable consent enforcement, auditable execution, and deterministic reconstruction of AI-assisted decisions while preserving regulatory alignment and clinical usability. In a medical imaging proof-of-concept spanning nine simulated scenarios and approximately 43,500 inference attempts across cohorts of 50 to 1000 subjects, the framework achieved a mean Governance Quality Index of 0.93, governance overhead below 11 ms per operation under routine settings, and throughput above 220 requests per second on average. Complementary validation over a real-world paired imaging-and-clinical dataset structure further showed that the same governance abstractions can be instantiated for 625 subjects and 6349 MR images. Overall, the framework separates governance from data and computation, providing verifiable auditability and reproducibility without disrupting existing clinical infrastructures.
Panagoulias et al. (Fri,) studied this question.