Developing Deep-Learning Models in the Hospital

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, as it requires components such as GPU compute, high-speed networking, fast storage, and policies and procedures around usage.

By stepping through the typical project workflow at the MGH & BWH Center for Clinical Data Science (CCDS), this paper presents the following:

  • The reasons for building such a system on-premises
  • The challenges that must be confronted
  • A case study in how such tooling is leveraged across the project lifecycle.
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