Innovation

What could be next? Preview DNA’s Season 4 exploring the future

Preview the trailer for Season 4 of the Vanderbilt Health DNA podcast series. Explore issues facing businesses across industries, and how VUMC is experimenting with and designing solutions for problems — old and new — by supporting smart people with visionary ideas. 

Four named Master Innovators at Vanderbilt

Richard Caprioli, C. David Weaver, Susan Eagle, and Franz Baudenbacher as Master Innovators at Vanderbilt.

A probe-based system developed by a team at Vanderbilt University and Vanderbilt University Medical Center uses near-infrared autofluorescence to confirm the location of parathyroid tissue during surgery.

Study shows Vanderbilt-developed technology assists surgeons in identifying parathyroids

A team of surgeons and biomedical engineers have shown the use of probe-based near infrared autofluorescence technology helps confirm the identification of parathyroid glands during endocrine surgery

The Alliance for Genomic Discovery announces founding biopharma members: AbbVie, Amgen, AstraZeneca, Bayer and Merck

Illumina Inc., in collaboration with Nashville Biosciences LLC, a leading clinical and genomic data company and wholly owned subsidiary of Vanderbilt University Medical Center, have announced the five founding new members of the Alliance for Genomic Discovery.

Ann Walker, Research Assistant III, spins blood samples on a carousel prior to DNA extraction.

Alliance for Genomic Discovery FAQs

Here are frequently asked questions about Vanderbilt University Medical Center’s Alliance for Genomic Discovery.

Project aims to improve ear disease diagnoses with objective machine learning techniques

A new project, funded by the National Institutes of Health (NIH), National Institute on Deafness and Other Communication Disorders (NIDCD) and led by Aaron Moberly, MD, and Metin Gurcan, PhD, professor and director of the Center for Biomedical Informatics and the Clinical Image Analysis Lab at Wake Forest University School of Medicine, aims to develop machine learning applications to analyze eardrum videos collected with a digital otoscope.

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