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The use of data for medical scientific research offers great potential for society as a whole, as the evaluation of large volumes of data with machine learning methods can result in new research approaches as well as new methods of diagnostics or treatment. However, the use of such data often fails due to high prerequisites or unclear requirements of data protection law.Processing of radiology data, such as MRI brain scans, is tied to specific risks for data subjects. This complicates the processing of such data for research purposes. Data trustees can help to reduce these risks through offering independent anonymization and pseudonymization services as well as secure processing environments in which health data is stored only for the time required for processing and analysis and is subsequently deleted.Thus, the use of data trustees can help to comply with data protection law, with risk-reduction being considered in favor of processing in decisions weighing the interests for and against data processing as necessary to comply with Art. 9 Para. 2 lit. j, Art. 89 Para 1 of the General Data Protection Regulation (GPDR) in conjunction with national law, or when assessing the compatibility of the secondary purpose of the processing with the purpose of collection. In this respect, the use of data trust models can contribute to the reduction of hurdles for the data processing of health data for scientific research purposes.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10834543 | PMC |
| http://dx.doi.org/10.1007/s00103-023-03807-z | DOI Listing |
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