Google has gained access to a huge trove of US patient data - without the need to notify those patients - thanks to a deal with a major health firm. The scheme, dubbed Project Nightingale, was agreed with Ascension, which hopes to develop artificial intelligence tools for doctors. Google can access health records, names and addresses without telling patients, according to the Wall Street Journal, which first reported the news. Google said it was "standard practice". Among the data the tech giant reportedly has access to under the deal are lab results, diagnoses, records of hospitalisation and dates of birth.
For a long time, financial institutions had a buttoned-down reputation when it came to innovative thinking. Nowadays, even the most conventional and risk-averse parts of the economy are looking at Artificial Intelligence, not long ago considered an experimental, bleeding edge technology. Wall Street is the financial district of New York City. It is the home of the New York Stock Exchange, the world's largest stock exchange by market capitalization of its listed companies. Nowhere is the change more dramatic than in Financial Services.
David Feinberg, Google's Vice President of Healthcare, recently described "a search bar on top of ... [ ] your [electronic health records] that needs no training," on stage at a conference in Las Vegas. Google is testing a service that would use its search and artificial intelligence technology to analyze patient records for Ascension, the largest nonprofit health system in the U.S., according to documents about the efforts reviewed by Forbes. Called "'Nightingale," the Google-Ascension project indicates that Google's push into health analysis is farther along than previously believed, even as the company has faced a growing backlash over health-related privacy concerns. Ascension said in a statement that all its work with Google complies with privacy law and is "underpinned by a robust data security and protection effort, which Google echoed in its own blog post later Monday, including that "patient data cannot and will not be combined with any Google consumer data. " The Wall Street Journal first published details of the Ascension partnership earlier on Monday.
Cnvrg.io, which is developing a data science platform through auto-adaptive and continual machine learning, today announced raising $8 million in the completion of its seed and Series A funding rounds. Led by Hanaco VC, the latest round follows the company's seed funding round, led by Jerusalem Venture Partners. The company said the new funding will allow the company to open offices in New York and expand its sales and research and development efforts. It serves companies across several industries, including financial services, insurance, health care, retail, automotive, gaming, manufacturing, and media. "As data scientists and AI consultants ourselves, we understand the frustration data scientists, data engineers and organizations encounter when building machine learning," said Yochay Ettun, CEO and co-founder of cnvrg.io.
New York, Nov 13 (IANS) Scientists are now working to apply Artificial Intelligence (AI) to psychiatry, with a speech-based mobile app that can categorize a patient"s mental health status as well as or better than a human can. "We are not in any way trying to replace clinicians," said Peter Foltz, research professor at the Institute of Cognitive Science at University of Colorado at Boulder. "But we do believe we can create tools that will allow them to better monitor their patients," he added in a paper published in Schizophrenia Bulletin. Even when a patient does make it in for an occasional visit, therapists base their diagnosis and treatment plan largely on listening to a patient talk – an age-old method that can be subjective and unreliable, notes paper co-author Brita Elvevåg, a cognitive neuroscientist at the University of Tromsø, Norway.
AI has moved into the art world. Two paintings up for auction in New York highlight a growing interest in artificial intelligence-created works – a technique that could transform how art is made and viewed but is also stirring up passionate debate. Last year, the art world was stunned when an AI painting sold for US$432,500, and auctioneers are keen to further test demand for computer-generated works. "Art is a true reflection of what our society, what our environment responds to," said Max Moore of Sotheby's. Sotheby's will put two paintings by the French art collective Obvious up for sale this week, including "Le Baron De Belamy."
New reports indicate that Google has been secretly collecting US patients' data from over 2,600 hospitals in order to train its new AI project. A recent report published by The Wall Street Journal indicates that Google has been collecting medical data on millions of US citizens through something known as the'Project Nightingale.' According to the report, the project had started in secret at some point in 2018, in collaboration with a St. Louis-based healthcare system known as Ascension. The system includes a chain of around 2,600 Catholic hospitals, all of which have handed over private medical information to Google's new AI initiative. Of course, the company had a reason behind collecting all of this data, and the reason was to create and test new software.
New York-based startup Barsys recently announced the Coaster, a smart saucer that guides the user on how to create a particular cocktail. By connecting the coaster to a smartphone with the complimentary app downloaded, users can choose from Barsys' existing library of cocktails or input their own recipe. Once a cocktail is selected, the user can simply start pouring ingredients one by one, and with each new alcohol or mixer, the coaster will light up when the correct amount has been added to the glass. The Barsys Coaster is regularly priced at US$149 (S$203), but interested customers can pre-order it here for US$95 (S$130). The device will start shipping in December.
New York– After looking at standard ECG tests, Artificial Intelligence (AI) can help identify patients most likely to die of any medical cause within a year, claim researchers. To reach this conclusion, researchers from Geisinger Health System in Pennsylvania analyzed the results of 1.77 million ECGs and other records from almost 400,000 patients. The team used this data to compare machine learning-based models that either directly analyzed the raw ECG signals or relied on aggregated human-derived measures (standard ECG features typically recorded by a cardiologist) and commonly diagnosed disease patterns. The neural network model that directly analyzed the ECG signals was found to be superior for predicting one-year risk of death. Surprisingly, the neural network was able to accurately predict risk of death even in patients deemed by a physician to have a normal ECG.