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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 3, september 2020
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 3, september 2020
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    Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence

    Autor: 
    Jennath, H. S.
    ;
    Anoop, V S
    ;
    Asharaf, S
    Fecha: 
    09/2020
    Palabra clave: 
    blockchain; health; data security; data privacy; artificial intelligence; interoperability; traceability; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12763
    DOI: 
    https://doi.org/10.9781/ijimai.2020.07.002
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2791
    Open Access
    Resumen:
    Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models.
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    • vol. 6, nº 3, september 2020

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