Suchi Saria, the John C. Malone Assistant Professor of computer science, statistics and health policy at Johns Hopkins University spoke at the 2019 Future of Individualized Medicine conference, hosted by the Scripps Research Translational Institute. Saria discussed her research in applying machine learning approaches to individualizing healthcare.
Artificial intelligence is gradually changing medical practice. With the recent progress in data collection, advanced machine learning methods, and high-performance computing infrastructure, AI can assist in disease diagnosis, treatment selection, and prediction of patients' clinical outcomes.
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Artificial intelligence will change lives in many ways. Already, AI solutions are being deployed and having significant impact in healthcare. Daniel Kraft, MD, Chair for Medicine, Singularity University, shares his expert views on how significant this technology will be in finding the right diagnosis and therapies and shifting the ‘practice’ of medicine to the real ‘science’ of medicine.
XPRIZE is an educational (501c3) nonprofit organization whose mission is to bring about radical breakthroughs for the benefit of humanity, thereby inspiring the formation of new industries and the revitalization of markets that are currently stuck due to existing failures or a commonly held belief that a solution is not possible. XPRIZE addresses the world's Grand Challenges by creating and managing large-scale, high-profile, incentivized prize competitions that stimulate investment in research and development worth far more than the prize itself. It motivates and inspires brilliant innovators from all disciplines to leverage their intellectual and financial capital.
Faster medical treatment saves lives. Machine Learning is already saving lives, by scouring a multitude of patients’ data and comparing them to one patient’s health data to detect symptoms 12 to 24 hours sooner than a doctor could. "In many pressing medical problems, the answers to knowing whom to treat, when to treat, and what to treat with, might already be in your data" says Suchi Saria. Learn how TREWS (Targeted Real-time Early Warning Score) is leading the way to save lives.
Suchi Saria is a professor of computer science and health policy, and director of the Machine Learning and Health Lab at Johns Hopkins University. Her research is focused on designing data solutions for providing individualized care.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx