Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging
Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging
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Publisher
London: Nature Publishing Group UK
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Language
English
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London: Nature Publishing Group UK
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Background
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates that DP has negati...
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Full title
Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging
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TN_cdi_doaj_primary_oai_doaj_org_article_5debbebf04fd4e9d94555bf1fa6d9f67
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_5debbebf04fd4e9d94555bf1fa6d9f67
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ISSN
2730-664X
E-ISSN
2730-664X
DOI
10.1038/s43856-024-00462-6
