AI & Data Exchange 2026: NIH’s Susan Gregurick on overcoming data silos with AI analytics
AI & Data Exchange 2026: NIH’s Susan Gregurick on overcoming data silos with AI analytics
Publish Date: 2026-05-08 14:18:00
Source Domain: federalnewsnetwork.com
Here is a summary of the article about the National Institutes of Health’s (NIH) use of artificial intelligence, with key points highlighted in an unordered list.
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Breaking Down Data Silos: NIH is leveraging AI to merge and analyze health data from disconnected systems, facilitating a robust understanding of public health issues like the impact of COVID-19 on cancer progression.
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Handling Massive Datasets: NIH manages a vast amount of data—nearly 440 petabytes. It is using AI to make sense of this data and standardize it for use in analytics. Programs like Bridge to AI are helping create high-quality, AI-ready datasets.
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Enhancing Operational Efficiency: AI is streamlining the processing of grant applications, clustering them for appropriate review sections, and helping avoid conflicts of interest among reviewers.
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Partnerships with Technology Giants: Collaborations with cloud service providers such as AWS, Google, and Microsoft Azure have enhanced NIH’s data infrastructure, providing scalable environments for testing AI algorithms and analytics.
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Real-time Analytics and Visualization: AI-driven dashboards are generating real-time visualizations of health trends, like patient diagnoses, which help understand larger shifts in health conditions.
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Training Next Generation Talent: NIH’s AIM-AHEAD program aims to bring AI literacy to upcoming researchers, clinicians, and public health workers to foster the development of future AI technologies tailored to healthcare.
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Building a Data-driven Health System: The integration of AI, data analysis, and cross-sector partnerships is shaping a more responsive and data-driven healthcare system, enabling real-time insights and testing advanced health learning models.
