Transparent and Reproducible AI-based Medical Imaging Pipelines Using the Cloud

Abstract Artificial intelligence (AI) algorithms hold the potential to revolutionize medical imaging. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address this issue remains challenging. Here, we discuss the potential of cloud-based infrastructures for implementing and sharing transparent and reproducible AI-based medical imaging pipelines. Starting from a published cancer imaging study investigating an AI biomarker for the prognostication of non-small cell lung cancer patients, we demonstrate end-to-end reproducibility of the research from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. By leveraging cloud-based platforms, using data hosted by the Imaging Data Commons (IDC) combined with code and compute resources, we make every step of the analysis and the resulting data easy to access, reproduce, and share. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines in medical imaging, which can accelerate the translation into clinical solutions.