Deep Learning in Healthcare Summit
Boston

Since joining DeepMind two years ago, Jane's non-machine learning background has equipped her with a unique set of tools and perspectives for tackling the hardest AI problems. “It’s exhilarating to formulate theories of human brain function as powerful deep reinforcement learning models that can solve similarly complex tasks,” she shares.

A multi-institutional team spanning has reported the first successful genome surgery, changing how the genome is folded inside the nucleus. The advance may lead to new methods of understanding and overcoming genetic diseases.

Ucare.ai is focused on applying AI on the healthcare system to increase efficiencies and help patient coverage. In particular, the company uses deep learning and neural network algorithms to predict healthcare patterns in patients, and beyond, to reduce preventable hospitalization, and, in turn, save on costs and hassles.

Soon, Boston-area radiologists will have AI “assistants” integrated into their daily workflows, helping them more quickly and accurately diagnose disease from MRIs, CAT scans, X-rays and more. The trained neural networks residing on DGX-1 systems in CCDS’s data center are in a constant state of learning, continually ingesting countless medical images worldwide.

Accidental falls among people aged 65 years and older caused approximately 2,700,000 injuries, 27,000 deaths, and cost more than 34 billion dollars in the US annually in recent years. Here, we derive and validate a predictive model for falls based on a retrospective cohort of those 65 years and older.