UC San Francisco's Center for Digital Health Innovation and GE Healthcare today announced a partnership to develop a library of deep learning algorithms - complex problem-solving formulas - that will empower clinicians to make faster and more effective decisions about the diagnosis and management of patients with some of the most common and complex medical conditions. This Smart News Release features multimedia. View the full release here: http://www.businesswire.com/news/home/20161115006296/en/ The first wave of algorithms aims to expedite differential diagnosis in acute situations such as trauma, to speed treatment, improve survival and reduce complications. These algorithms can be deployed worldwide via the GE Health Cloud and smart GE imaging machines, sharing the research of healthcare leaders with clinicians around the world who have varied expertise. The algorithms will be used to ensure providers around the world can access new knowledge and insights delivered through deep learning - a method by which machines can rapidly generate new levels of clinical and operational value from large imaging and textual data sets in ways that traditional machine learning methods cannot. "With this partnership, we have the opportunity to leverage the technical expertise of one of the largest providers of medical technology globally and the clinical and research expertise of UCSF, one of the largest recipients of National Institutes of Health (NIH) funding, in order to make the promise of precision healthcare a reality," said Michael Blum, MD, associate vice chancellor for informatics, director of CDHI and professor of medicine at UCSF. "Next generation data science techniques have already transformed the industrial and consumer world. With this collaboration, these technologies will be applied to our clinical data and images to provide clinicians with actionable information in near real-time. Together, we will develop tools and algorithms that will allow clinicians and researchers to identify problems and ask questions that are only achievable with vast computing power and datasets."