{"id":32662,"date":"2026-05-08T21:15:14","date_gmt":"2026-05-08T21:15:14","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/32662\/"},"modified":"2026-05-08T21:15:14","modified_gmt":"2026-05-08T21:15:14","slug":"ai-data-exchange-2026-nihs-susan-gregurick-on-overcoming-data-silos-with-ai-analytics","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/32662\/","title":{"rendered":"AI & Data Exchange 2026: NIH\u2019s Susan Gregurick on overcoming data silos with AI analytics"},"content":{"rendered":"

The National Institutes of Health is leaning into artificial intelligence to gather insights from a vast amount of health data \u2014 a shift that could allow the agency to conduct research more quickly and offer new tools to support clinicians.<\/p>\n

Susan Gregurick, associate director for data science at NIH, said during Federal News Network\u2019s AI & Data Exchange 2026<\/a> that advances in AI are beginning to unlock insights from data across disconnected systems.<\/p>\n

\u201cI think that there\u2019s just an unlimited amount of excitement here,\u201d Gregurick said. \u201cI\u2019ve seen some really exciting trends in developing AI technologies that can extract very difficult-to-find data from clinical records and notes that doctors take.\u201d<\/p>\n

AI to speed public health responses<\/p>\n

At the core of NIH\u2019s AI push is a push to break down data silos that have limited how quickly researchers can respond to emerging public health issues.<\/p>\n

NIH is using AI in partnership with the Energy Department and the National Cancer Institute to extract information from pathology reports. Through this project, NIH is helping researchers get a better understanding of the relationship between COVID infections and cancer progression.<\/p>\n

\u201cThe challenges we get really have to deal with getting real-time data about health,\u201d she said. \u201cIt was very hard to understand the relationship between people who might have had COVID and the effect it would have on cancer progression,\u201d Gregurick said.<\/p>\n

\u201cIf you are a cancer patient, your diagnosis is going to come from a pathology report that\u2019s in a completely different system than your electronic health record data. That program really does extract out the pertinent information from the pathology reports and then feeds it into our system for understanding cancer.\u201d<\/p>\n

Mining massive datasets<\/p>\n

Beyond extracting insights from individual records, NIH is also grappling with the sheer scale of its data environment. NIH has about 440 petabytes of data across its three cloud service providers.<\/p>\n

\u201cThis is really an impactful amount of data \u2014 getting our arms around all that data, discovering it, finding it, using it in analytics. That\u2019s something that I\u2019m very passionate about and spending a lot of time on,\u201d Gregurick said. \u201cThere is an opportunity for using this sort of real-world data, like wearables and survey data, to help us understand health outcomes. Challenges persist in the structure of that data. Just getting it into standard formats from different devices is going to be an interesting amount of work.\u201d<\/p>\n

One example is the agency\u2019s Bridge to AI program, which focuses on creating high-quality, AI-ready datasets.<\/p>\n

\u201cThe whole goal of this program is to generate that AI-ready, high-quality, gold standard data that could then be amenable to new AI models,\u201d she said.<\/p>\n

NIH, through its Bridge to Artificial Intelligence (Bridge2AI<\/a>) program, is generating new flagship datasets and best practices for machine learning analysis. These datasets were collected and processed with AI modeling in mind. One of the first datasets Bridge2AI focused on was the prevalence of Type 2 diabetes in American Indian and Alaska Native populations.<\/p>\n

\u201cWe know that there\u2019s a higher prevalence of Type 2 diabetes in those populations, and so creating the data helps researchers understand why these communities are at higher risk and how we can treat them,\u201d Gregurick said. \u201cThis is one of the benefits of working together across NIH \u2014 creating that data and then making it available for researchers in populations such as American Indian, Alaska Native, or people who are from disadvantaged communities across the country.\u201d<\/p>\n

Using AI to improve back-office processes at NIH<\/p>\n

AI is also playing a growing role in NIH\u2019s internal operations, particularly in managing the approximately 20,000 grant applications the agency receives in a year.<\/p>\n

\u201cWe\u2019re using AI to do things like take grant applications that come in and cluster them into certain bins that would be appropriate for different study sections, so we don\u2019t have to manually read the grants right off the bat,\u201d Gregurick said. \u201cWe can use a large language model to cluster these into a relevant study section. And AI and large language models can also help us identify if are there any potential conflicts that reviewers may have with a grant, so they\u2019re not assigned a conflicting grant, and help us select reviewers as well.\u201d<\/p>\n

NIH is also investing in the next generation of AI talent through programs like AIM-AHEAD, a community of over 10,000 AI and healthcare experts.<\/p>\n

\u201cThe whole goal of AIM-AHEAD is to ensure that younger researchers, new clinicians or old ones and public health workers have access to and can get training on artificial intelligence,\u201d Gregurick said.<\/p>\n

Partnering with cloud providers<\/p>\n

Partnerships with industry are another cornerstone of NIH\u2019s AI strategy. Cloud providers have helped the agency scale its data infrastructure and provide training environments for researchers.<\/p>\n

\u201cThe partnerships with AWS, Google and Microsoft Azure have been tremendous for us in terms of getting all of our data into cloud,\u201d Gregurick said. \u201cEach of the cloud service providers has built out a sandbox for students or for investigators to come and benchmark and try different algorithms, pull different datasets or just learn how to use the cloud to implement much higher throughput pipelines of analytics.\u201d<\/p>\n

AI is also enabling new types of real-time analytics and visualization, which she said could transform how health systems respond to emerging trends.<\/p>\n

\u201cIf I wanted to know how many patients in the last 90 days have been diagnosed with lupus across our health network, having an AI-integrated dashboard with visualizations and analytics that actually can help us understand and pull that data in real time and show it to us is a game changer in terms of understanding larger trends,\u201d Gregurick said.<\/p>\n

Testing a health learning system in real time<\/p>\n

The combination of AI, data integration and cross-sector collaboration is laying the foundation for a more data-driven health system.<\/p>\n

\u201cWorking with startup companies or even larger ones, to really help us visualize these trends, is providing us insight that we just didn\u2019t have before,\u201d she said. \u201cIt\u2019s allowing us to test what a health learning system that\u2019s focused on patient information looks like in real time.\u201d<\/p>\n

While data challenges remain, NIH sees AI as essential to its mission moving forward.<\/p>\n

\u201cGathering and harmonizing health data to identify trends, it continues to be a grand challenge,\u201d Gregurick said. \u201cWe have a long way to go.\u201d<\/p>\n

But with AI increasingly embedded across research, operations and partnerships, she expects the agency can close that gap and deliver more precise insights into the nation\u2019s most pressing health issues.<\/p>\n

Discover more articles and videos now on the AI & Data Exchange event page<\/a>.<\/p>\n

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