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. Researchers from the University of Colorado Anschutz Medical Campus will play roles in two major components of this project, leveraging the innovative work being done in the CU Department of Biomedical Informatics in enabling team science and establishing standards necessary to advance the role of AI in healthcare research in a nationwide effort.
“It is an honor to work with the NIH and institutions across the country in exploring the expansion of AI in research, utilizing our expertise in developing best practices in a national effort to make progress in addressing a variety of challenges in health research,” said Monica Munoz-Torres, PhD, visiting associate professor of the Translational and Integrative Sciences Lab (TISLab) in the Department of Biomedical Informatics at the University of Colorado School of Medicine, and lead for the operational unit in charge of standards for the project. “AI will play a critical role in advancing our understanding of human health, and this is a critical step forward in establishing how it can be most beneficial.”
Anne Thessen, PhD, also a visiting associate professor at TISLab at the University of Colorado School of Medicine, will lead the team organizational unit of the project. Melissa Haendel, PhD (chief research informatics officer at CU Anschutz); Sean Davis, MD, PhD (professor and Rifkin and Bennis Endowed chair of cancer informatics and deputy director, Center for Health Artificial Intelligence); and Casey Greene, PhD (chair of the Department of Biomedical Informatics) will also play critical roles in this project.
Data sets will target pressing health challenges
This program will also 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.
“It is an honor to work with the NIH and institutions across the country in exploring the expansion of AI in research, utilizing our expertise in developing best practices in a national effort to make progress in addressing a variety of challenges in health research." — Monica Munoz-Torres, PhD
“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 Acting NIH Director Lawrence Tabak, DDS, PhD. “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.
Program Targets Diverse Research Teams
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 will issue 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.
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