Machine learning and artificial intelligence (AI) are exploding in popularity in fields ranging from art to science and everything in between—bioengineering included. While these tools have the potential to bring about significant improvements in healthcare, the systems aren’t perfect. How can we identify when machine learning and artificial intelligence are suggesting solutions that aren’t effective in the real world? 

Mayo Clinic and Illinois researchers are turning to Yellowstone National Park to unlock the secrets of kidney stones.

Dr. Bobo has collaborated with Mayo Clinic and University of Illinois Urbana-Champaign (Illinois) researchers to develop a model that combines genomics and clinical assessments to uncover patterns of how patients respond to antidepressants.

A collaboration between the National Center for Supercomputing Applications (NCSA) at the University of Illinois, Mayo Clinic, and the University of Michigan are introducing a new machine-learning-driven approach to latent tuberculosis infection (LTBI) diagnostics. While leveraging a high throughput detection technology and powerful bioinformatics, this approach aims to reveal multi-marker signatures for LTBI diagnosis and risk stratification.

The 2018 Computational Genomics Course, sponsored by the Mayo Clinic & Illinois Alliance for Technology-Based Healthcare, was held in June at three locations - Mayo Clinic’s campuses in Rochester, Minnesota; and Jacksonville, Florida; and at the Carl R. Woese Institute for Genomic Biology at the University of Illinois.

The Mayo Clinic Center for Individualized Medicine is collaborating with the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign to unleash the potential of artificial intelligence in patient care. Funded by a National Science Foundation (NSF) grant, the Mayo Clinic and University of Illinois Alliance and corporate partners are conducting research into the big data challenge: how to develop computer systems that, combined with human intelligence, unlock new analysis of health and disease.