  Clinical Artificial lntelligence (AI) applications are rapidly expanding
worldwide, and have the potential to impact to all areas of medical practice.
Medical imaging applications constitute a vast majority of approved clinical AI
applications. Though healthcare systems are eager to adopt AI solutions a
fundamental question remains: \textit{what happens after the AI model goes into
production?} We use the CheXpert and PadChest public datasets to build and test
a medical imaging AI drift monitoring workflow to track data and model drift
without contemporaneous ground truth. We simulate drift in multiple experiments
to compare model performance with our novel multi-modal drift metric, which
uses DICOM metadata, image appearance representation from a variational
autoencoder (VAE), and model output probabilities as input. Through
experimentation, we demonstrate a strong proxy for ground truth performance
using unsupervised distributional shifts in relevant metadata, predicted
probabilities, and VAE latent representation. Our key contributions include (1)
proof-of-concept for medical imaging drift detection that includes the use of
VAE and domain specific statistical methods, (2) a multi-modal methodology to
measure and unify drift metrics, (3) new insights into the challenges and
solutions to observe deployed medical imaging AI, and (4) creation of
open-source tools that enable others to easily run their own workflows and
scenarios. This work has important implications. It addresses the concerning
translation gap found in continuous medical imaging AI model monitoring common
in dynamic healthcare environments.
