  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. By leveraging more than 590,000 chest
radiographs from five institutions, we evaluated the efficacy of DP-enhanced
domain transfer (DP-DT) in diagnosing cardiomegaly, pleural effusion,
pneumonia, atelectasis, and in identifying healthy subjects. We juxtaposed
DP-DT with non-DP-DT and examined diagnostic accuracy and demographic fairness
using the area under the receiver operating characteristic curve (AUC) as the
main metric, as well as accuracy, sensitivity, and specificity. Our results
show that DP-DT, even with exceptionally high privacy levels (epsilon around
1), performs comparably to non-DP-DT (P>0.119 across all domains). Furthermore,
DP-DT led to marginal AUC differences - less than 1% - for nearly all
subgroups, relative to non-DP-DT. Despite consistent evidence suggesting that
DP models induce significant performance degradation for on-domain
applications, we show that off-domain performance is almost not affected.
Therefore, we ardently advocate for the adoption of DP in training diagnostic
medical AI models, given its minimal impact on performance.
