| Title: | Ethical considerations on the use of big data and artificial intelligence in kidney research from the ERA ethics committee |
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| Authors: | ID Van Biesen, Wim (Author)
ID Buturović-Ponikvar, Jadranka (Author)
ID Fontana, Monica (Author)
ID Heering, Peter (Author)
ID S. Sever, Mehmet (Author)
ID Sawhney, Simon (Author)
ID Luyckx, Valerie (Author) |
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| Files: |  PDF - Presentation file, download (705,70 KB)
MD5: C220FF2A72AB90AA102D437B4AC00BC3
 URL - Source URL, visit https://academic.oup.com/ndt/advance-article/doi/10.1093/ndt/gfae267/7906607
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| Language: | English |
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| Typology: | 1.02 - Review Article |
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| Organization: |  UKC LJ - Ljubljana University Medical Centre
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| Abstract: | In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The associated ethical question is whether and how we as a nephrology community can and should encourage the collection of big data of our patients. We identify some ethical reflections on the use of big data, and their importance and relevance. Furthermore, we balance advantages and pitfalls and discuss requirements to make legitimate and ethical use of big data possible. The collection, organization, and curation of data come upfront in the pipeline before any analyses. Great care must therefore be taken to ensure quality of the data at this stage, to avoid the ‘garbage in garbage out’ problem and suboptimal patient care as a consequence of such analyses. Access to the data should be organized so that correct and efficient use of data is possible. This means that data must be stored safely, so that only those entitled to do so can access them. At the same time, those who are entitled to access the data should be able to do so in an efficient way, so as not to hinder relevant research. Analysis of observational data is itself prone to many errors and biases. Each of these biases can finally result in provision of low-quality medical care. Secure platforms should therefore also ensure correct methodology is used to interpret the available data. This requires close collaboration of a skilled workforce of experts in medical research and data scientists. Only then will our patients be able to benefit fully from the potential of AI and big data. |
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| Keywords: | artificial intelligence, big data, machine learning, observational trial |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 455-464 |
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| Numbering: | Vol. 40, ǂiss.ǂ3 |
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| PID: | 20.500.12556/DiRROS-29146  |
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| UDC: | 61 |
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| ISSN on article: | 1460-2385 |
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| DOI: | 10.1093/ndt/gfae267 |
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| COBISS.SI-ID: | 226854403 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 20.2.2025;
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| Publication date in DiRROS: | 22.04.2026 |
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| Views: | 84 |
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| Downloads: | 62 |
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