October 5, 2022
Dr. Pamela Payne-Foster, professor of community medicine and population health with the College of Community Health Sciences, is part of a University of Alabama at Birmingham research team recently awarded funding as part of the National Institutes of Health’s newly launched Bridge2AI (Artificial Intelligence) program.
The aim of the Bridge2AI program will be to accelerate the widespread use of artificial intelligence by biomedical and behavioral research communities, with $130 million being invested over the next four years.
The UAB research teaming module for this project will include: module principal investigator Dr. Jake Chen with the Informatics Institute at UAB; co-principal investigator Dr. Ying Ding with the University of Texas at Austin; Dr. Swathi Thaker with the Center for Clinical and Translational Sciences at UAB; and Payne-Foster, who will be the head of Diversity, Equity, and Inclusion.
As part of this intelligence program, UAB’s research team will assist in the development of guidance and standards relating to innovative AI-ready data sets. There is hope that these data sets will potentially assist in solving various human health challenges, including uncovering how genetic, behavioral and environmental factors can influence a person’s physical condition throughout their life.
“I’m very excited for this opportunity because this is the first time UAB is participating in such a program, along with the fact that NIH is one of the first organizations to push biomedical sciences and collect data that will be ready for the AI mission learning community,” Chen said.
UAB is scheduled to receive just over $2 million for its portion of the program.
Although some artificial intelligence is already being used in biomedical research and health care, there have been limitations in its widespread adoption due to challenges of applying AI technologies to diverse data types, for a broader context of use. This has primarily been attributed to the previously insufficient collection of routine biomedical and behavioral data sets, which lacked important contextual information about the data type, collection conditions or other factors. Without this information, AI technologies cannot accurately analyze and interpret data, and can unintentionally incorporate bias or inequities.
To address this issue, UAB’s research team will assist in producing a variety of diverse data types to be used by the research community for AI analyses, focusing specifically on underprivileged and minority communities. This process will also include voice and other data to help identify abnormal changes in the body. AI-ready data will be prepared to help improve decision making in order to accelerate recovery from acute illnesses and to reveal the biological processes underlying an individual’s recovery from illness.
