AI Deployed in Health Care for Drug Discovery, Data and Imaging

Nvidia is helping facilitate the adoption of digital health agents to deploy AI in the U.S. health care system

Heidi Vella, Freelance journalist

October 14, 2024

3 Min Read
Artificial Intelligence Healthcare Medical Research Data Science
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Digital health agents are being increasingly adopted in the U.S. health care system to leverage AI for various applications, including drug discovery, data extraction and 3D CT image organization, according to Nvidia

The company’s NIM cloud-native microservices is being used to facilitate this AI model deployment, it said. 

NIM is a set of optimized cloud-native microservices designed to shorten time-to-market and simplify deployment of generative AI models.

Examples of NIM applications being used in health care include researchers from the National Cancer Institute deploying several AI models built with Nvidia MONAI for medical imaging. VISTA-3D NIM foundation model is being used for segmenting and annotating 3D CT images.

In addition, generative virtual screening for drug discovery using three NIM microservices is helping researchers search and optimize libraries of small molecules to identify promising candidates that bind to a target protein.

Nvidia recently released two new NIM microservices for drug discovery to help researchers understand how proteins bind to target molecules, a crucial step in drug design.

These are the AlphaFold2-Multimer NIM microservice that Nvidia said helps researchers accurately predict protein structure from their sequences in minutes, reducing the need for time-consuming tests in the lab. 

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The RFdiffusion NIM microservice which uses generative AI to design novel proteins that are promising drug candidates because they’re likely to bind with a target molecule.

Nvidia said NIM and NIM Agent Blueprints can help medical researchers across the public sector jump-start their adoption of state-of-the-art, optimized AI models to accelerate their work.

The pretrained models are customizable based on an organization’s own data and can be continually refined based on user feedback, it added. 

By conducting more preclinical research digitally, scientists can narrow down their pool of drug candidates before testing in the lab — making the discovery process more efficient and less expensive.

For example, a team at NIH’s National Center for Advancing Translational Sciences (NCATS) is using the NIM Agent Blueprint for generative AI-based virtual screening to reduce the time and cost of developing novel drug molecules

The Genetic and Rare Diseases Information Center, also run by NCATS, is exploring using the PDF data extraction blueprint to develop generative AI tools that enhance the center’s ability to glean information from previously unsearchable databases.

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Massive quantities of health care data — including research papers, radiology reports and patient records — are unstructured and locked in PDF documents, making it difficult for researchers to quickly search for information. 

“The center analyzes data sources spanning the National Library of Medicine, the Orphanet database and other institutes and centers within the NIH to answer patient questions,” said Sam Michael, chief information officer of NCATS. 

“AI-powered PDF data extraction can make it massively easier to extract valuable information from previously unsearchable databases.”  

Companies such as Abridge and HealthOmics are also using NVIDIA applications to win government contracts, the company added.

About the Author

Heidi Vella

Freelance journalist, Freelance

Heidi is an experienced freelance journalist and copywriter with over 12 years of experience covering industry, technology and everything in between.

Her specialisms are climate change, decarbonisation and energy transition and she also regularly covers everything from AI and antibiotic resistance to digital transformation. 

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