Securing Collaborative Medical AI by Using Differential Privacy: Domain Transfer for Classification of Chest Radiographs

Purpose: To investigate the integration of differential privacy (DP) and analyze its impact on model performance as compared with models trained without DP. Materials and Methods: Leveraging more than 590 000 chest radiographs from five institutions, including VinDr-CXR from Vietnam, ChestX-ray14 and CheXpert from the United States, UKA-CXR from Germany, and PadChest from Spain, the authors evaluated the efficacy of DP -enhanced domain transfer (DP -DT) in classifying cardiomegaly, pleural effusion, pneumonia, atelectasis, and healthy individuals. Diagnostic performance and sex -specific and age -specific demographic fairness of DP -DT and of non-DP-DT models were compared using the area under the receiver operating characteristic curve (AUC) as the main metric, as well as accuracy, sensitivity, and specificity as secondary metrics, and evaluated for statistical significance using paired Student t tests. Results: Even with high privacy levels (epsilon approximate to 1), DP -DT showed no evidence of differences compared with non-DP-DT in terms of a decrease in AUC of cross -institutional performance as compared with single -institutional performance (VinDr-CXR: 0.07 vs 0.07, P = .96; ChestX-ray14: 0.07 vs 0.06, P = .12; CheXpert: 0.07 vs 0.07, P = .18; UKA-CXR: 0.18 vs 0.18, P = .90; and PadChest: 0.07 vs 0.07, P = .35). Furthermore, AUC differences between DP -DT and non-DP-DT models were less than 1% for all sex subgroups (P > .33 for female and P > .22 for male, for all domains) and nearly all age subgroups (P > .16 for younger participants, P > .33 for adults, and P > .27 for older adults, for nearly all domains). Conclusion: Cross -institutional performance of artificial intelligence models was not affected by DP.