As the field of deep learning in medicine progresses from research to clinical deployment, practical considerations quickly become a primary concern for operational leadership. Hardware infrastructure, although a key enabler, presents unique challenges in the clinical arena.
By stepping through the typical project workflow at the MGH & BWH Center for Clinical Data Science (CCDS), this paper explores the reasons for building such a system on-premises, the challenges that must be confronted, and a case study in how such tooling is leveraged across the project lifecycle.