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

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-preserving techniques. In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training.

Methods

We used two datasets: (1) A large dataset (N = 193,311) of high quality clinical chest radiographs, and (2) a data

SUBMITTER: Tayebi Arasteh S 

PROVIDER: S-EPMC10940659 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature