This blog was authored by NIH staff who serve on the Bridge to Artificial Intelligence (Bridge2AI) Working Group.
In April 2021, we introduced NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) program to tap the potential of artificial intelligence (AI) for revolutionizing biomedical discovery, increasing our understanding of human health, and improving the practice of medicine. In the past year, Bridge2AI researchers have been creating guidance and standards for the development of ethically sourced, state-of-the-art, AI-ready data sets to help solve some of the most pressing challenges in human health such as uncovering how genetic, behavioral, and environmental factors influence health and wellness. The program will also support the training required to enable the broader biomedical and behavioral research community to leverage AI technologies.
The NIH initiative will support diverse teams and tools to ensure that data sets adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Beyond ensuring compliance to FAIR principles, Bridge2AI will develop and disseminate best practices that promote a culture of diversity and continuous ethical inquiry into how data are collected.
The Bridge2AI program will support innovative data-generation projects nationwide to collect complex AI-ready data in four biomedical areas:
Clinical Care Informatics—Intensive care units treat patients with urgent medical conditions such as sepsis and cardiac arrest. This data generation project will collect, integrate, annotate, and share high-resolution physiological data from adult and pediatric critical care patients from 14 health systems that can then be used by AI technologies to identify approaches to improve recovery from acute illness.
Functional Genomics—Within each cell in the human body lies a wealth of information about health, disease, and the impact of environmental factors. This project will generate richly detailed proteomic, genomic, and cellular imaging data to help predict disease mechanisms and associated gene pathways and networks for a variety of health outcomes.
Precision Public Health—The human voice is as unique as a fingerprint and has been found to contain acoustic signatures of human health and disease. This project will collect large-scale multimodal data sets containing voice, genomic, and clinical data, which AI technologies can use to help improve screening for and the diagnosis and treatment of a variety of developmental, neurological, and mental health conditions.
Return to Health—Much can be learned by uncovering how individuals move from a less healthy to a healthier state, a process called salutogenesis. This project will collect data from a diverse population with varying stages of type 2 diabetes to help improve our understanding of chronic disease progression and recovery. To learn more about Bridge2AI and salutogenesis, please view Bridging Our Way to Health Restoration by Helene M. Langevin, MD, director of the National Center for Complementary and Integrative Health.
To support these data generation projects, the Bridge2AI program includes a BRIDGE Center with a range of expertise to support interdisciplinary team science. The center will facilitate development of cross-cutting products such as standards harmonization, ethical AI best practices, and workforce development opportunities for the research community.
One of the goals of Bridge2AI is to foster a culture that will identify, assess, and address ethical issues as an integral part of creating AI-ready data sets. Ethical considerations include informed consent, data privacy, bias in data, and its impact on fairness and trustworthiness of AI applications, equity, and justice, and inclusion and transparency in design.
Every component of the Bridge2AI program includes a plan for incorporating diverse perspectives at every step. The BRIDGE Center will serve as a hub for supporting ethical and trustworthy AI development across Bridge2AI with the goal of providing tools, best practices, and resources to address cross-cutting biomedical challenges.