Operationalizing AI powered medical imaging pipeline for cohort building

Authors: Jared Erwin , Senior Software Engineer, HLS Nursing AI and Data Platform, Faculty UW School of Medicine Manoj Kumar , Director, HLS - Data & AI HLS Frontiers AI Alberto Santamaria-Pang , Principal Applied Data Scientist, HLS Frontiers AI and Adjunct Faculty, Johns Hopkins Medicine Overview In Part 1 , of this series, we showed how natural language could be used to define medical imaging cohorts and retrieve relevant studies in seconds instead of months. That proof-of-concept demonstrated the value of the idea — but not how to make it repeatable, or production-ready. This post focuses on how we turned that prototype into a production-oriented Azure Machine Learning pipeline — to scale execution and produce clear, versioned artifacts that could drive an interactive cohort exploration UI.