Ping An Insurance (Group) Company of China, Ltd. (hereafter "Ping An" or the "Group", HKEX: 2318; SSE: 601318) is pleased to announce Ping An Global Voyager Fund is leading an investment of US$15 Million in Riverain Technologies, a leading provider of clinical artificial intelligence software used to efficiently detect lung disease at its earliest stages. Riverain Technologies markets advanced artificial intelligence imaging software used by leading hospitals around the world. The software significantly improves a clinician's ability to accurately and efficiently detect cancer and other cell anomalies in thoracic CT and X-ray images. The company's suite of patented ClearReadTM software tools are FDA-cleared, deployable in the clinic or in the cloud, and powered by the most advanced artificial intelligence and machine learning methods available to the medical imaging market. Its products are relied upon by leading healthcare institutions, including Duke University, Mayo Clinic, University of Chicago, University of Michigan, and Veterans Affairs hospitals.
Erik Learned-Miller is one reason we talk about facial recognition at all. In 2007, years before the current A.I. boom made "deep learning" and "neural networks" common phrases in Silicon Valley, Learned-Miller and three colleagues at the University of Massachusetts Amherst released a dataset of faces titled Labelled Faces in the Wild. To you or me, Labelled Faces in the Wild just looks like folders of unremarkable images. You can download them and look for yourself. There's boxer Joe Gatti, gloves raised mid-fight.
The US Food and Drug Administration (FDA) has cleared an artificial intelligence (AI) algorithm from GE Healthcare that analyzes chest x-rays for pneumothorax and helps flag suspected cases for radiologists to prioritize reading, the company announced today. The algorithm, part of a set of other quality-assurance algorithms named the Critical Care Suite, was developed to run on a GE Healthcare mobile x-ray device. The software is not yet for sale, and an outside expert expressed concern about its false positive rate. The idea for the application came from bedside clinician experience of waiting for radiologists to read chest x-rays, said Rachael Callcut, MD, MSPH, a surgeon and director of data science for the Center for Digital Health Innovation at the University of California, San Francisco. UCSF proposed developing the feature as part of a development partnership with GE Healthcare.
The need for new medications is higher than ever, but so is the cost and time to bring them to market. Developing a new drug can cost billions and take as long as 14 years, according to the U.S. Food and Drug Administration. Yet with all that effort, only 8 percent of drugs make it to market, the FDA said. "We need to make smarter decisions about which potential medicines we develop and test," said Abraham Heifets, co-founder of San Francisco-based startup Atomwise. The six-year-old company, a member of our Inception startup incubator program, is working to make that happen by using GPU-accelerated deep learning to predict which molecules are most likely to lead to treatments.
Want to know what's in your genes? People in the US will soon be able to buy a genetic test that tells them how likely they are to develop 10 diseases, including late-onset Alzheimer's. The saliva-based test is being marketed by 23andMe, a company based in California. The firm already offers "spit kits" for US-based customers who want to find out about their ancestry or risk of passing on certain genetic diseases to their children. But in 2013, the Food and Drug Administration banned 23andMe from offering a test that assessed a person's genetic risk for 254 disorders and conditions.