Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy

Understand

Developing robust and effective artificial intelligence (AI) models in medicine requires access to large amounts of patient data.

• The use of AI models solely trained on large multi-institutional datasets can help with this, yet the imperative to ensure data privacy remains, particularly as membership inference risks breaching patient confidentiality.
• As a proposed remedy, we advocate for the integration of differential privacy (DP).
• We specifically investigate the performance of models trained with DP as compared to models trained without DP on data from institutions that the model had not seen during its training (i.e., external validation) - the situation that is reflective of the clinical use of AI models.