Securing AI-Based Diagnostic Models Using Distributed Ledgers

Abstract

Artificial intelligence (AI) now frequently supports high-stakes diagnostic decisions in radiology, pathology, cardiology, dermatology, and beyond. Yet the same models are exposed to integrity risks (data poisoning, adversarial perturbations), intellectual-property (IP) theft, provenance gaps, and governance liabilities, especially when models are trained across institutions and deployed at the edge. This manuscript proposes and details a comprehensive, ledger-centric security blueprint for AI diagnostics that couples permissioned distributed ledgers with privacy-preserving federated learning (FL), confidential computing, secure aggregation, model provenance artifacts, and IP protection (watermarking/fingerprinting). We synthesize the literature on blockchain-enabled learning and medical AI threats, align the design with NIST AI RMF and ISO/IEC 23894 risk management expectations, and map controls to emerging regulatory obligations (EU AI Act; HIPAA Security Rule modernization). We then present a systems methodology—DLT-MedGuard—covering threat models, data and model lineage capture, consent and access control via smart contracts, on-chain attestation of trusted execution environments, and reproducible release management via model cards and dataset datasheets anchored on-chain. A statistical analysis using an illustrative evaluation shows that DLT-MedGuard can preserve diagnostic performance while reducing successful poisoning and model-extraction rates, at modest latency overheads suitable for clinical settings. We conclude with a forward research agenda spanning post-quantum security, zero-knowledge attestations, energy-aware consensus, formal verification of smart contracts, and harmonized cross-border compliance. Overall, distributed ledgers do not merely “store hashes”; when designed as a verifiable provenance and control fabric, they materially raise the security baseline for AI-based diagnostics without undermining clinical utility.