FAYETTEVILLE, Ark. – With supplemental funding from the National Institutes of Health, a team of researchers led by Justin Zhan, professor of data science at the University of Arkansas, will collaborate with NIH and Google software engineers to build cloud-based learning modules for biomedical research.
These modules will help educate biomedical researchers on the ways that artificial intelligence and machine learning, both rapidly becoming important tools in biomedical research, can enhance and streamline data analysis for different types of medical and scientific images.
The new funding, $140,135, has been awarded through the National Institute of General Medical Sciences’ Institutional Development Award Program. Zhan partnered with Kyle Quinn, associate professor of biomedical engineering, and Larry Cornett, director of the Arkansas IDeA Network of Biomedical Research Excellence at the University of Arkansas for Medical Sciences, which is administering the grant.
In addition to the Arkansas IDeA Network’s support, case studies for the learning modules will be developed with support from the data science and the imaging and spectroscopy cores of the Arkansas Integrative Metabolic Research Center.
Justin Zhan and Kyle Quinn.
“Big data is transforming health and biomedical science,” Zhan said. “The new technology is rapidly expanding the quantity and variety of imaging modalities, for example, which can tell doctors so much more about their patients. But this transformation has created challenges, particularly with storing and managing massive data sets. Also, while the big data revolution transforms biology and medicine into data-driven sciences, traditional education is responding slowly. Addressing this shortcoming is part of what we’re trying to do.”
The researchers will secure the technical expertise and resources needed to provide training to students and health-care professionals on the use of artificial intelligence and machine learning, as they apply to biomedical research.
Artificial intelligence is the ability of computer systems to perform tasks that have traditionally required human intelligence. One example of artificial intelligence is machine learning, in which algorithms and computations become more accurate than humans at predicting outcomes. This process demands tremendous computational power, more than standard computer clusters can handle.
The Arkansas researchers will parter with software engineers at Google and the National Institute of General Medical Sciences to address the computational requirements of artificial intellegence-driven research through the use of cloud computing. Cloud computing provides access to computing services over the internet, allowing faster and more flexible solutions in biomedical research.