  Adoption of artificial intelligence medical imaging applications is often
impeded by barriers between healthcare systems and algorithm developers given
that access to both private patient data and commercial model IP is important
to perform pre-deployment evaluation. This work investigates a framework for
secure, privacy-preserving and AI-enabled medical imaging inference using
CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically
secure 2-party Computation (2PC) protocols between the machine learning model
vendor and target patient data owner. A common DenseNet-121 chest x-ray
diagnosis model was evaluated on multi-institutional chest radiographic imaging
datasets both with and without CrypTFlow2 on two test sets spanning seven sites
across the US and India, and comprising 1,149 chest x-ray images. We measure
comparative AUROC performance between secure and insecure inference in multiple
pathology classification tasks, and explore model output distributional shifts
and resource constraints introduced by secure model inference. Secure inference
with CrypTFlow2 demonstrated no significant difference in AUROC for all
diagnoses, and model outputs from secure and insecure inference methods were
distributionally equivalent. The use of CrypTFlow2 may allow off-the-shelf
secure 2PC between healthcare systems and AI model vendors for medical imaging,
without changes in performance, and can facilitate scalable pre-deployment
infrastructure for real-world secure model evaluation without exposure to
patient data or model IP.
