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Published in: Insights into Imaging 1/2021

Open Access 01-12-2021 | Artificial Intelligence | Statement

ESR white paper: blockchain and medical imaging

Author: European Society of Radiology (ESR)

Published in: Insights into Imaging | Issue 1/2021

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Abstract

Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.
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Metadata
Title
ESR white paper: blockchain and medical imaging
Author
European Society of Radiology (ESR)
Publication date
01-12-2021
Publisher
Springer International Publishing
Published in
Insights into Imaging / Issue 1/2021
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-021-01029-y

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