In recent years, artificial intelligence (AI) has started to change many parts of healthcare. AI foundational models made for specific fields like genomics, drug discovery, and clinical diagnostics are now changing how medical research and practice work. In the United States, this change is strong because of the country’s large healthcare system, research centers, and drug companies.
This article explains how domain-specific AI foundational models are created and used to speed up genomics, drug development, and diagnostic work. It focuses on those who run hospitals and healthcare systems — like hospital leaders, practice owners, and IT staff — who help bring in new technology to improve patient care and hospital management.
A big reason AI is growing in US healthcare is NVIDIA, a tech company known for its graphics chips and now for AI tools. NVIDIA has teamed up with health groups and companies like IQVIA, Illumina, the Mayo Clinic, and the Arc Institute to develop AI applications in medicine faster.
These partnerships use NVIDIA’s powerful computers and AI tools to build AI models for tasks in medicine. These models are large AI systems trained to understand complex health data and help make good predictions and decisions.
Together, these partnerships show a wide effort to make AI models that help with genome study, speed drug discovery, and improve medical diagnostics in the US healthcare system.
Genomics studies an organism’s complete set of DNA, including all its genes. It is very important in modern medicine, especially in making treatments fit each person’s needs, called precision medicine. But genomic data is huge and complicated, making it hard to process and understand.
NVIDIA’s technology enables fast and scalable genomic analysis using GPU-accelerated systems. For example, Illumina’s DRAGEN software uses this to quickly turn raw sequencing data into high-quality genetic variant information. This helps find mutations linked to diseases faster.
These advances cut down delays in genomic research and clinical tests. This allows healthcare providers in the US to give genetic tests that are faster and more accurate. The analysis includes different biological data types – genomics (DNA), transcriptomics (RNA), and proteomics (proteins) – to give a full view of a patient’s biology.
This mix of data is important to improve personalized treatments and patient results, especially as large health systems handle data from many kinds of patients.
Drug discovery usually takes a long time and costs a lot. It involves finding molecules, testing them, and running clinical trials. AI is changing this by helping screen and check drug candidates much faster.
AI methods like machine learning and deep learning analyze molecular structures, predict how drugs will interact with targets, and create new molecules with desired features before making them in the lab. AI helps quickly screen large chemical databases to find promising drugs.
NVIDIA’s AI platform supports drug research by building biomolecular foundation models that predict protein structures and help with docking ligands — small molecules that bind to proteins. This technology can create and rank drug candidates, making drug research less about trial and error.
One problem in using AI for drug development is sharing data while protecting intellectual property and keeping results accurate. Addressing this is important for US pharmaceutical companies and labs as they try to use AI more without risking their data and ideas.
New diagnostic technologies are getting better at finding and watching diseases like cancer. Digital pathology means scanning tissue slides at very high detail and turning them into digital images. Checking these images by hand takes time and can vary between experts.
The Mayo Clinic uses NVIDIA’s DGX Blackwell systems to build AI pathology models trained on millions of images and patient records. These models help pathologists find small patterns and unusual signs, leading to faster and more accurate diagnoses.
Medical imaging like CT scans, MRIs, and endoscopies also create large amounts of images. AI models can automate tasks like finding areas of interest, diagnosing, and aligning images for comparison. This helps make diagnosis more precise and speeds up work.
In the US, where hospitals and diagnostic centers see many patients, AI can make these processes smoother, improve consistency in diagnoses, and reduce the workload on specialists.
AI foundational models are not just for research. They are also used in daily clinical work to reduce the paperwork and routine tasks so healthcare workers can spend more time with patients.
NVIDIA’s CEO Jensen Huang called AI agents a “digital workforce.” These agents can break down complex tasks, find needed information, and give proper responses automatically. They can do things like:
In clinical trials, AI agents trained on large health data sets help find patients quickly, match them to trial criteria, and manage paperwork, making the process faster.
Medical administrators and IT teams in US health systems can use AI-powered automation to improve how work is done and lower mistakes from handling documents and communications by hand.
The use of “test-time scaling” — which means using more computing power when AI models run — is expected to grow. This will let many AI models operate at once in the background, helping healthcare providers handle more tasks at the same time. This improves clinical and office work.
Domain-specific AI models are trained on health-related data and understand medical terms and details better than general AI models. This makes them more accurate and reliable at helping health workers.
For US medical practices, this means:
Practices using these AI tools can stay competitive and meet growing needs for precise medicine and faster test results.
Using AI in healthcare has challenges. Medical leaders and IT experts need to think about:
Knowing these points well is important to make AI work well in medical practices and health organizations.
With ongoing funding and partnerships like those between NVIDIA and US health institutions, AI foundational models will become more common in genomics, drug discovery, and diagnostics.
AI agents will serve as digital helpers in clinical work. They will reduce slow parts of the process and let human staff focus on complex decisions and patient care. Fast processing of genomics and multiomics data will help make treatments more personal and improve health results.
As biomedical AI develops, domain-specific foundational models will help the US lead in modern healthcare and research, benefiting patients, doctors, and scientists.
Health organizations should not only create AI models but also add them smoothly into everyday work. AI automation goes beyond research into clinical and front-office tasks.
In US medical offices, tasks like scheduling appointments, answering calls, patient communication, and insurance checks take up a lot of time and resources. AI tools like those from companies specializing in phone automation can help make these tasks easier.
By using AI agents that understand healthcare, practices can automate appointment reminders, do patient pre-screening over the phone, and send calls to the correct departments automatically. This cuts wait times, lowers missed calls, and ensures patients get timely help.
Also, AI can link with electronic health records to give clinical staff quick summaries or highlight urgent issues, improving workflow.
For IT managers and practice leaders in the US, using workflow automation with domain-specific AI makes work simpler, lowers costs, and improves patient experience.
Through building and using AI foundational models made for biomedical uses, the US healthcare sector is moving toward faster genomics analysis, quicker drug discovery, and better diagnostics. Along with workflow automation, these tools are changing how medical practices work, helping administrators, IT staff, and clinical workers.
NVIDIA’s partnerships with IQVIA, Illumina, Mayo Clinic, and Arc Institute focus on accelerating biomedical AI in genomics, drug discovery, and clinical diagnostics, highlighting a shift towards AI agents that autonomously streamline workflows and reduce administrative burdens in healthcare.
AI agents are digital workforce systems that reason about missions by breaking tasks down, retrieving data, or using tools to generate quality responses, working autonomously alongside human employees to enhance efficiency.
IQVIA uses NVIDIA AI Foundry services to develop domain-specific AI foundation models and agents trained on vast healthcare datasets, aiming to streamline clinical trial processes such as patient recruitment and regulatory submissions.
Illumina integrates GPU-accelerated computing into its DRAGEN sequencing software to efficiently manage expanding multiomics datasets, accelerating analysis in genomics, transcriptomics, and proteomics for precision medicine.
Mayo Clinic implements NVIDIA DGX Blackwell systems for AI-driven digital pathology, leveraging large-scale correlated slide images and patient records to develop foundation models that enhance pathological analysis accuracy.
Arc Institute develops large-scale biological AI models that integrate DNA, RNA, and protein data using NVIDIA’s BioNeMo and DGX Cloud infrastructure to advance synthetic biology and drug discovery research.
NVIDIA’s foundational architecture includes NIMs—pre-optimized AI microservices, Nemo—a generative AI training framework, and Blueprints—reference implementations for healthcare workflows facilitating rapid, optimized deployment.
AI agents reduce administrative workload by automating complex tasks such as patient recruitment and regulatory compliance, improving operational efficiency, and potentially shortening the drug development timeline.
AI agents are expected to scale inferencing computations massively, enabling multiple AI models to work simultaneously behind the scenes, fundamentally transforming clinical workflows and research through increased automation and intelligence.
AI agents enable faster, scalable multiomics data analysis crucial to precision medicine, facilitating timely, personalized treatment decisions by efficiently interpreting complex biological data sets in genomics and proteomics.
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