Running Local LLMs Behind Institutional Firewalls: Hands-On Guide for Secure Clinical AI Learning Lab

Large language models are increasingly used across radiology, pathology, and clinical workflows, but cloud-based models often conflict with patient privacy, HIPAA requirements, and institutional data governance. This Learning Lab addresses the growing need to deploy LLMs securely behind institutional firewalls while maintaining control, transparency, and compliance.

Participants learn when and why local LLM deployment makes sense, including key trade-offs between cloud and on-premise models. Attendees explore practical considerations such as model selection, deployment architecture, and security constraints that shape real-world clinical implementations.

In this interactive lab, attendees gain direct experience running local LLMs and applying them to imaging and clinical use cases. Participants leave with:

• A clear understanding of local LLM architecture 
• Practical experience running LLMs behind a firewall 
• Example workflows for radiology and pathology use 
• A deployment checklist and vetted open-source tools  

This Learning Lab is designed for both technical and clinical participants, with no prior LLM experience required.

Objectives 

• Describe the key architectural components and governance considerations required to deploy large language models securely within an institutional environment. 
• Compare cloud-based and locally deployed LLM approaches based on privacy, compliance, cost, and workflow integration needs. 
• Implement a basic local LLM workflow to support radiology or pathology use cases while maintaining data security and regulatory compliance.
  Demonstrate how to install, configure, and run a local large language model behind an institutional firewall using open-source tools. 
• Create a simple LLM-enabled workflow that summarizes or extracts key information from radiology or pathology text using secure internal data. 
• Practice designing a deployment plan that accounts for infrastructure constraints, governance requirements, and validation considerations within a healthcare environment.