Cornell Research Group Explores Potential of Machine Learning in Medicine

Medicine and artificial intelligence are ever-evolving fields at the forefront of scientific discovery. A new Cornell research group — Machine Learning in Medicine — aims to coalesce the two, with the goal of improving methods for disease detection and diagnosis. This endeavor is a collaboration between faculty at Cornell Tech and Weill Cornell Medicine, bringing together “researchers with common interests and complementary expertise.” MLIM’s work is primarily an interdisciplinary dialogue, bridging campuses and research fields. 

“The idea was to link people with a machine learning background in Ithaca to [people working with] clinical data and hypotheses at Weill,” said Prof. Amy Kuceyeski, mathematics and radiology, one of the organizing members of the group. While Kuceyeski’s background is in mathematics, she started learning methods for modeling biological systems as a postdoctoral researcher at Weill. Seeing this as an area for innovation, Kuceyeski helped establish MLIM in 2018.

New Multidisciplinary Project Team Tackles Current Biomedical Issues

At the intersection of medical research and engineering, Cornell University Biomedical Devices Team works on various projects that can be applied in real-world medical settings. Founded in 2018 by a group of biology students eager for more hands-on work, the team has now grown to three subteams, Product Development, Policy and Practices and Business, consisting of members from many different colleges. “The founding members and I were from Cornell Surgical Society. It was a group of us with a passion for medicine interested in designing surgical devices for possible competitions,” CUBMD project team lead Oscar Liu ’21 said. CUBMD’s goal is to design effective biomedical devices that can conceivably be used in real-life healthcare settings.