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Titel:

Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging.

Dokumenttyp:
Journal Article
Autor(en):
Tayebi Arasteh, Soroosh; Ziller, Alexander; Kuhl, Christiane; Makowski, Marcus; Nebelung, Sven; Braren, Rickmer; Rueckert, Daniel; Truhn, Daniel; Kaissis, Georgios
Abstract:
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 negative implications on model accuracy and fairness, which are unacceptable in medicine and represent a main barrier to the widespread use of privacy-preserv...     »
Zeitschriftentitel:
Commun Med (Lond)
Jahr:
2024
Band / Volume:
4
Heft / Issue:
1
Volltext / DOI:
doi:10.1038/s43856-024-00462-6
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/38486100
TUM Einrichtung:
Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski); Institut für KI und Informatik in der Medizin (Prof. Rückert)
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