NIH Strategically, and Ethically, Building a Bridge to AI (Bridge2AI)
Helene Langevin, M.D.
April 7, 2021
This message is reposted from the National Library of Medicine’s Musings From the Mezzanine. This piece was authored by staff across NIH that serve on the working group for the NIH Common Fund’s Bridge2AI program—a new trans-NIH effort to harness the power of AI to propel biomedical and behavioral research forward.
The evolving field of Artificial Intelligence (AI) has the potential to revolutionize scientific discovery from bench to bedside. The understanding of human health and disease has vastly expanded as a result of research supported by the National Institutes of Health (NIH) and others. Every discovery and advance in contemporary medicine comes with a deluge of data. These large quantities of data, however, still result in restricted, incomplete views into the natural processes underlying human health and disease. These complex processes occur across the “health-disease” spectrum over temporal scales—sub-seconds to years—and biological scales—atomic, molecular, cellular, organ systems, individual to population. AI provides the computational and analytical tools that have the potential to connect the dots across these scales to drive discovery and clinical utility from all of the available evidence.
A new NIH Common Fund program, Bridge to Artificial Intelligence (Bridge2AI), will tap into the power of AI to lead the way toward insights that can ultimately inform clinical decisions and individualize care. AI, which encompasses many methods, including modern machine learning (ML), offers potential solutions to many challenges in biomedical and behavioral research.
AI emerged in the 1960s and has evolved substantially in the past two decades in terms of its utility for biomedical research. The impact of AI for biomedical and behavioral research and clinical care derives from its ability to use computer algorithms to quickly find connections from within large data sets and predict future outcomes. AI is already used to improve diagnostic accuracy, increase efficiency in workflow and clinical operations, and facilitate disease and therapeutic monitoring, to name a few applications. To date, the U.S. Food and Drug Administration has approved more than 100 AI-based medical products.
AI-assisted learning and discovery is only as good as the data used to train it.
The use of AI/ML modeling in biomedical and behavioral research is limited by the availability of well-defined data to “train” AI algorithms to learn how to recognize patterns within the data. Existing biomedical and behavioral data sets rarely include all necessary information as they are collected on relatively small samples and lack the diversity of the U.S. population. Data from a variety of sources are necessary to characterize human health, such as those from -omics, imaging, behavior, and clinical indicators, electronic health records, wearable sensors, and population health summaries. The data generation process itself involves human assumptions, inferences, and biases that must be considered in developing ethical principles surrounding data collection and use. Standardizing collection processes is challenging and requires new approaches and methods. Comprehensive, systematically generated and carefully collected data is critical to build AI models that provide actionable information and predictive power. Data generation remains among the greatest challenges that must be resolved for AI to have a real-world impact on medicine.
Bridge2AI is a bold new initiative at NIH designed to propel research forward by accelerating AI/ML solutions to complex biomedical and behavioral health challenges whose resolution lies far beyond human intuition. Bridge2AI will support the generation of new biomedically relevant data sets amenable to AI/ML analysis at scale; development of standards across multiple data sources and types; production of tools to accelerate the creation of FAIR (Findable, Accessible, Interoperable, Reusable) AI/ML-ready data; design of skills and workforce development materials and activities; and promotion of a culture of diversity and ethical inquiry throughout the data generation process.
- Patricia Flatley Brennan, R.N., Ph.D., Director, National Library of Medicine
- Michael F. Chiang, M.D., Director, National Eye Institute
- Eric Green, M.D., Ph.D., Director, National Human Genome Research Institute
- Helene Langevin, M.D., Director, National Center for Complementary and Integrative Health
- Bruce J. Tromberg, Ph.D., Director, National Institute of Biomedical Imaging and Bioengineering