This project focuses on integrating MAIA, a state-of-the-art Medical Artificial Intelligence toolkit, with BIANCA, utilizing a Kubernetes-based cluster to streamline sensitive clinical research workflows. MAIA is designed for cloud computing environments, enabling researchers to manage the entire AI lifecycle, including DICOM image storage, image annotation, model training, and deployment of AI models. The Kubernetes infrastructure allows scalable and efficient deep learning model development tailored to healthcare applications. At the core of this project is the use of sensitive clinical images retrieved from a PACS DICOM server, with secure handling of the data throughout the process. The aim is to explore how BIANCA can be integrated into this pipeline to facilitate deep learning model training, particularly when working with sensitive medical data. This involves addressing key technical aspects such as secure data transfer between MAIA and BIANCA, managing the trained models, and instantiating model training within the BIANCA environment. The research will also investigate the potential of incorporating high-performance computing (HPC) resources available through BIANCA into the AI workflow. By leveraging BIANCA’s vast computing capacity, the project aims to enhance the power and efficiency of deep learning model training, demonstrating how HPC can accelerate the AI research process in clinical settings. This collaboration between MAIA and BIANCA will highlight how Kubernetes-based cloud computing, integrated with HPC resources, can offer powerful, scalable solutions for AI-driven medical research while ensuring data privacy and security in compliance with healthcare regulations.