Artificial Intelligence at NIH

The National Institutes of Health (NIH) makes a wealth of biomedical data available to research communities and aims to make these data findable, accessible, interoperable, and reusable—or FAIR. Additionally, NIH seeks to make these data usable with artificial intelligence and machine learning (AI/ML) applications. The ability to apply AI/ML techniques to biomedical research data has the potential to improve health and health care system operations, as well as increase the delivery of high-quality health care and positive patient health outcomes.

The National Institute of Biomedical Imaging and Bioengineering (NIBIB) defines AI and its components as:

Artificial Intelligence: A feature where machines learn to perform tasks, rather than simply carrying out computations that are input by human users. Machine Learning: An approach to AI in which a computer algorithm (a set of rules and procedures) is developed to analyze and make predictions from data that is fed into the system. Neural Networks: A machine learning approach modeled after the brain in which algorithms process signals via interconnected nodes called artificial neurons. Deep Learning: A form of machine learning that uses many layers of computation to form what is described as a deep neural network, capable of learning from large amounts of complex, unstructured data.

Large datasets are central to the integration of AI in science and medicine, but many lack data on race, ethnicity, and social determinants of health, including considerations around minority health and health disparities in epidemiologic studies and prevention, diagnostic, and treatment interventions using AI.

AI applications could be particularly beneficial in places with limited access to health care, such as patients and populations in middle and low resource areas. Researchers are exploring clinical applications of AI; clinicians use AI to continuously learn and understand their patients; patients use AI to better understand themselves; society uses AI to complement and enhance human intelligence, rather than replace it; policymakers regulate AI to ensure its ethical and safe use.

 

Trans-NIH Initiatives

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To catalyze new opportunities in AI and data science, the NIH Office of Data Science Strategy (ODSS) coordinates activities across NIH’s 27 institutes and centers. Learn more »

 

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The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) program will propel biomedical research forward by setting the stage for widespread adoption of artificial intelligence (AI) that tackles complex biomedical challenges beyond human intuition. Learn more »

 

Funding Opportunities

  • Due May 14: Administrative Supplements for Workforce Development at the Interface of Information Sciences, Artificial Intelligence and Machine Learning (AI/ML), and Biomedical Sciences (NOT-OD-21-079
  • Due May 26: Notice of Special Interest (NOSI): Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data (NOT-OD-21-094)
  • Notice of Intent to Publish a Funding Opportunity Announcement for NIH Bridge2AI Integration, Dissemination, and Evaluation (BRIDGE) Center (U54) (NOT-RM-21-021)
  • Notice of Intent to Publish a Research Opportunity Announcement for the Data Generation Projects of the NIH Bridge to Artificial Intelligence (Bridge2AI) Program (OT2) (NOT-RM-21-022)
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Funded Research and Related Activities

Across NIH’s 27 institutes and centers, AI/ML technologies are being developed

  • to detect and predict disease progression.
  • for personalized therapies.
  • for precision control (e.g., prosthetics).
  • for automated monitoring of health.
  • to identify risk and target intervention.
  • for basic research (e.g., improving genome annotations with proteomic data).
  • for use in healthcare.

Below are some specific examples:

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News, Events, and Publications

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This page last reviewed on May 1, 2021