Published February 16, 2024
The role of artificial intelligence in improving all aspects of health care is the theme of a research symposium to be held from 1:30-6 p.m. Feb. 27 at the Jacobs School of Medicine and Biomedical Sciences at UB.
The event will kick off a new initiative at UB, which dedicates a total of $200,000 in combined seed funding for pilot research projects in the interdisciplinary areas of AI and health care. The funding comes from the Office of the Vice President for Research and Economic Development and the Office of the Vice President for Health Sciences.
Students from the School of Engineering and Applied Sciences will be shuttled from the North Campus to the Jacobs School for the event, reflecting the strong interdisciplinary nature of AI in health care at UB.
“UB researchers are already harnessing the power of AI in numerous disciplines,” says Venu Govindaraju, vice president for research and economic development. “Now, this funding will foster new cross-disciplinary collaborations that will bring that power to bear on detecting, treating and curing the most challenging human diseases.”
The goal of the pilot funding is to allow researchers to generate preliminary results to eventually attract additional funding from NIH and other federal agencies.
“Here at UB, with our strong foundation in health sciences, the newly created Institute for Artificial Intelligence and our robust engineering programs, we’re uniquely positioned to leverage AI’s potential for tackling the health challenges our society faces,” says Allison Brashear, vice president for health sciences and dean of the Jacobs School. “This symposium, part of our ongoing interdisciplinary series, is a testament to that commitment. We’re aiming to bridge fields across our strengths and set the foundation for new, collaborative advances in AI-powered health care.”
Mary Ellen Giger, Distinguished Service Professor of Radiology at the University of Chicago, will give the keynote speech. Giger is an expert in computer-aided diagnosis, including computer vision, machine learning and deep learning, in the areas of breast cancer, lung cancer, prostate cancer, lupus and bone diseases.
The schedule: