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The excitement around machine learning in medicine has permeated the world of clinicians and researchers. While artificial intelligence is unlikely to replace medical professionals in the near future, a new era of machine-learning applications to medicine is poised to revolutionize health care.

The Sept. 27-28 symposium “Bridging the Divide: Machine Learning in Medicine,” held at the Cornell Lab of Ornithology, brought together researchers and clinicians from Cornell’s Ithaca campus and Weill Cornell Medicine to discuss recent work and initiate collaborations in the field of machine learning in medicine.

“When I came to Cornell’s Ithaca campus after seven years at Weill, it was apparent to me the potential for fruitful collaborations between academics at Cornell in Ithaca and clinicians and researchers at Weill Cornell in the area of machine learning in health care. The interest we saw at the symposium, with over 100 in-person attendees and 60 remote attendees, was just the beginning,” said Amy Kuceyeski, an assistant professor of mathematics in radiology and in neuroscience at the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine and adjunct professor in biological statistics and computational biology in the College of Agriculture and Life Sciences.

The organizing team also included Mert Sabuncu, assistant professor of electrical and computer engineering and biomedical engineering; Martin T. Wells, the Charles A. Alexander Professor of Statistical Sciences and chair of the Departments of Statistical Science and Biological Statistics and Computational Biology; and James K. Min, professor of medicine and radiology and director of the Dalio Institute for Cardiovascular Imaging at NewYork-Presbyterian and Weill Cornell.

The keynote speaker, Dorin Comaniciu, vice president for artificial intelligence at Siemens Healthineers, talked about the applications of artificial intelligence technologies in health care targeting three key outcomes: patient experience, patient outcomes and quality of care.

In a poster session, 18 trainees from both campuses presented topics ranging from automated diagnosis of peripheral edema to neural network-based segmentation of 3D in vivo multiphoton images of cortical vasculature. Prizes were awarded to the top poster presenters: first prize was awarded to postdoctoral fellow Adrian Dalca (electrical and computer engineering) for his development of a fast algorithm for automated brain MRI registration; second prize was awarded to Casey Cazer (College of Veterinary Medicine) for her work using association rule mining to identify multidrug resistance patterns and third prize was awarded to postdoc Pegah Khosravi (Weill Cornell Medicine) for her work on deep neural network classification of pathology images.

Support for the Intercampus Machine Learning Symposium came from the Office of the Vice Provost for Academic Integration led by Dr. Gary Koretzky. Koretzky is currently focused on developing programs to enrich scholarship at Cornell by identifying opportunities and reducing barriers for collaborations.

“I was thrilled with the energy apparent at the Machine Learning Symposium. It was clear that investigators from New York City were learning about methods and approaches spearheaded by colleagues in Ithaca and that Ithaca faculty were already envisioning applications of their work to the clinical setting,” Koretzky said. “This symposium was precisely what we hoped for when we published the request for applications for intercampus symposia and serves as a model for future such events.”

Other departments from both campuses, including the Department of Radiology at Weill Cornell Medicine, the Department of Biological Statistics and Computational Biology and the Department of Electrical and Computer Engineering from Cornell-Ithaca, also provided support for the symposium.

The organizing team plans to continue the collaborations kindled at the symposium by hosting a wiki on its website, accessible to faculty and trainees from both campuses, listing proposals for projects looking for collaborators, grant opportunities, resources and events within the field of quantitative medicine.

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