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PLoS One . Integrating medical imaging datasets with blockchain wallets: A case study on ARDS-COVID19 patients

tetano

Editor, Senior Moderator
PLoS One


. 2026 Mar 18;21(3):e0338897.
doi: 10.1371/journal.pone.0338897. eCollection 2026.
Integrating medical imaging datasets with blockchain wallets: A case study on ARDS-COVID19 patients

Marco Esperança[SUP] 1 2 [/SUP], Tiago Galvão[SUP] 2 [/SUP], Diogo Freitas[SUP] 2 [/SUP], Joao C Ferreira[SUP] 1 2 3 [/SUP], Ana Cysneiros[SUP] 4 5 [/SUP], Luís Bento[SUP] 4 5 [/SUP]


Affiliations
Abstract

Medical imaging plays a critical role in diagnosing and managing acute conditions such as Acute Respiratory Distress Syndrome (ARDS), particularly in intensive care settings. However, radiological data are often siloed across Picture Archiving and Communication Systems (PACS), with limited interoperability, traceability, and patient control. This paper proposes and validates a blockchain-enabled architecture that integrates radiological imaging data into a patient-controlled digital wallet (BioWallet) ecosystem. The system combines verifiable credentials, decentralized identifiers (DIDs), and a FHIR-compliant backend to ensure secure, auditable, and standards-based access to DICOM images and associated metadata. The ledger stores only consent and audit hashes, while clinical data remain off-chain and correctable within FHIR/EHR systems, ensuring auditability without hindering rectification. A validation scenario replicating an ICU emergency was conducted using synthetic ARDS-COVID19 cases to assess latency, consent enforceability, and user experience. Results showed a 67% reduction in image access time compared to traditional systems, 100% success in blocking unauthorized access, and high clinician satisfaction. The architecture supports FAIR-compliant reuse of annotated imaging datasets, enhances transparency in image-driven research, and aligns with GDPR and future European digital identity frameworks. This work demonstrates the feasibility and value of a decentralized, patient-centric approach to imaging data governance in high-stakes clinical environments.


 
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