Federal Medical Scientists Are Using LLMs as Research Co-Pilots
Strong Data Governance Facilitates Effective LLM Use
In 2024, the U.S. Department of Energy-sponsored Argonne National Laboratory launched Argo, a custom interface that provides secure access to large language models such as Google’s Gemini and OpenAI’s GPT-5.4, says Matthew Dearing, the lab’s AI for operations technical lead.
The Lemont, Ill., research center’s goal is to provide the core technology that researchers need to leverage generative AI and LLMs for data analysis, content generation and other objectives, Dearing says.
Communication between LLMs, users and any software they build occurs on the lab’s network.
“In the early days, the main large language model companies made it very clear that if you gave them your prompts and data, they were going to save and reuse that in training the next versions of models,” Dearing says. “We either have the large language models running internally on systems that live inside Argonne that we can control, or we communicate with copies of large language models run through a secured network.”
READ MORE: Healthcare data governance is the foundation of modern medical research.
LLMs Require Centralized and Secure Data
Security was also a consideration for the Food and Drug Administration when building its LLM-based Elsa app.
Data that nonclinical laboratory and other study sponsors have submitted to the agency doesn’t meet the security requirements to be put into a government-issued version of a commercial LLM, says Chief AI Officer Jeremy Walsh.
“We had to say, ‘How do we get access to all of the model capabilities that exist and tailor the functionality specifically to the FDA’s use cases?’” he says. “We built out an application layer that sits between the users and models to give multimodal support for working with large documents — building out workspaces for them, doing custom prompt libraries.”
As Elsa — whose infrastructure involves Amazon Web Services and Google Cloud components — was being developed, the agency worked to centralize its internal divisions’ separate data silos, which included consolidating 40 application and submission systems into one platform.
“Right now, if you want to analyze an application, you’d have to get the files off a file-share, upload them to Elsa and then ask your questions,” Walsh says. “With the next release, you can just do it right from Elsa — tell it to analyze this application this way, and it will grab all of the data, process it and analyze it. We’re able to do things we couldn’t do last year because the data was all over the place.”
