Read and watch the video on the NIH website.  NIH launches Bridge2AI

The National Institutes of Health will invest $130 million over four years, pending the availability of funds, to accelerate the widespread use of artificial intelligence (AI) by the biomedical and behavioral research communities. The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) program is assembling team members from diverse disciplines and backgrounds to generate tools, resources, and richly detailed data that are responsive to AI approaches. At the same time, the program will ensure its tools and data do not perpetuate inequities or ethical problems that may occur during data collection and analysis. Through extensive collaboration across projects, Bridge2AI researchers will create guidance and standards for the development of ethically sourced, state-of-the-art, AI-ready data sets that have the potential to help solve some of the most pressing challenges in human health — such as uncovering how genetic, behavioral, and environmental factors influence a person’s physical condition throughout their life.

“Generating high-quality ethically sourced data sets is crucial for enabling the use of next-generation AI technologies that transform how we do research,” said Lawrence A. Tabak, D.D.S., Ph.D., Performing the Duties of the Director of NIH. “The solutions to long-standing challenges in human health are at our fingertips, and now is the time to connect researchers and AI technologies to tackle our most difficult research questions and ultimately help improve human health.”

AI is both a field of science and a set of technologies that enable computers to mimic how humans sense, learn, reason, and take action. Although AI is already used in biomedical research and healthcare, its widespread adoption has been limited in part due to challenges of applying AI technologies to diverse data types. This is because routinely collected biomedical and behavioral data sets are often insufficient, meaning they lack important contextual information about the data type, collection conditions, or other parameters. Without this information, AI technologies cannot accurately analyze and interpret data. AI technologies may also inadvertently incorporate bias or inequities unless careful attention is paid to the social and ethical contexts in which the data is collected. In order to harness the power of AI for biomedical discovery and accelerate its use, scientists first need well-described and ethically created data sets, standards, and best practices for generating biomedical and behavioral data that is ready for AI analyses.

As it creates tools and best practices for making data AI-ready, Bridge2AI will also produce a variety of diverse data types ready to be used by the research community for AI analyses. These types include voice and other data to help identify abnormal changes in the body. Researchers will also generate data that can be used to make new connections between complex genetic pathways and changes in cell shape or function to better understand how they work together to influence health. In addition, AI-ready data will be prepared to help improve decision making in critical care settings to speed recovery from acute illnesses and to help uncover the complex biological processes underlying an individual’s recovery from illness.

The Bridge2AI program is committed to fostering the formation of research teams richly diverse in perspectives, backgrounds, and academic and technical disciplines. Diversity is fundamental to the ethical generation of data sets, and for training future AI technologies to reduce bias and improve effectiveness for all populations, including those who are underrepresented in biomedical and behavioral research. Bridge2AI will develop ethical practices for data generation and use, addressing key issues such as privacy, data trustworthiness, and reducing bias.

NIH has issued four awards for data generation projects, and three awards to create a Bridge Center for integration, dissemination and evaluation activities. The data generation projects will generate new biomedical and behavioral data sets ready to be used for developing AI technologies, along with creating data standards and tools for ensuring data are findable, accessible, interoperable, and reusable, a principle known as FAIR. In addition, data generation projects will develop training materials that promote a culture of diversity and the use of ethical practices throughout the data generation process. The Bridge Center will be responsible for integrating activities and knowledge across data generation projects, and disseminating products, best-practices, and training materials.

The Bridge2AI program is an NIH-wide effort managed collaboratively by the NIH Common Fund, the National Center for Complementary and Integrative Health, the National Eye Institute, the National Human Genome Research Institute, the National Institute of Biomedical Imaging and Bioengineering, and the National Library of Medicine. To learn more about the Bridge2AI program, visit the Musings from the Mezzanine blog from the National Library of Medicine, and watch this video (Youtube) about the Bridge2AI program.

About the NIH Common Fund: The NIH Common Fund encourages collaboration and supports a series of exceptionally high-impact, trans-NIH programs. Common Fund programs are managed by the Office of Strategic Coordination in the Division of Program Coordination, Planning, and Strategic Initiatives within the NIH Office of the Director in partnership with the NIH Institutes, Centers, and Offices. More information is available at the Common Fund website: https://commonfund.nih.gov.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov.