The University of California, San Francisco, announced Friday, Oct. 11, that it’s launching a new center to accelerate the use of AI in radiology, and it’s targeting the opioid epidemic as one if its first challenges.
UCSF’s Center for Intelligent Imaging will partner with Santa Clara, California-based NVIDIA to help build AI tools that can be used in everyday practice. They’ve also scored millions in grants from the National Institutes of Health (NIH) to help develop more advanced methods of treating pain.
“Artificial intelligence represents the next frontier for diagnostic medicine,” Christopher Hess, MD, PhD, chair of the UCSF Department of Radiology and Biomedical Imaging, said in a school announcement “It is poised to revolutionize the way in which imaging is performed, interpreted and used to direct care for patients.”
Earlier this month, the university also announced that it is receiving 10 grants from the National Institutes of Health totaling $40 million. Those funds come as part of the NIH’s Helping to End Addiction Long-term initiative and will be used to study pain and corresponding opioid addiction. More than $4 million from those grants will be used by advanced imaging professor Sharmila Majumdar, PhD, to develop deep learning-based technologies to help reconstruct images more quickly, segment tissues and detect spinal degeneration. Their goal is to develop an integrated model for treating lower back pain, which is pervasive but difficult to address, often leading to pain pill addiction.
Majumdar—who will run the Center for Intelligent Imaging, also referred to as ci2—plans to use AI-powered algorithms, data analysis, brain imaging and biomechanical evaluation of the spine to better understand chronic back pain.
“The volume of medical imaging has been rapidly increasing and radiologists are struggling to keep up with the sheer number of images,” Majumdar said. “ci2 aims to impact the entire value chain of imaging, from the time the patient comes for a scan to the final delivery of individualized, quantitative, prognostic and care-defining information.”