The healthcare sector, that contains a diverse array of industries with activities ranging from research to manufacturing to facilities management (pharma, medical equipment, healthcare facilities), generated in 2013 something like 153 exabytes (1 exabyte 1 billion gigabytes). It is estimated that by year 2020 the healthcare sector will generate 2,134 exabytes. To put that into perspective data centres globally will have enough space only for an estimated of 985 exabytes by 2020. Meaning that two and a half times this capacity would be required to house all the healthcare data. Big data have four V's volume, velocity (real time will be crucial for healthcare), variety and veracity (noise, abnormality, and biases). Poor data quality costs the US economy $ 3,1 trillion a year. And 1 in 3 business leaders don't trust the information they use to make decisions, and this is true also for the healthcare sector.
On a bright Tuesday afternoon in Paris last fall, Alex Karp was doing tai chi in the Luxembourg Gardens. He wore blue Nike sweatpants, a blue polo shirt, orange socks, charcoal-gray sneakers and white-framed sunglasses with red accents that inevitably drew attention to his most distinctive feature, a tangle of salt-and-pepper hair rising skyward from his head. Under a canopy of chestnut trees, Karp executed a series of elegant tai chi and qigong moves, shifting the pebbles and dirt gently under his feet as he twisted and turned. A group of teenagers watched in amusement. After 10 minutes or so, Karp walked to a nearby bench, where one of his bodyguards had placed a cooler and what looked like an instrument case. The cooler held several bottles of the nonalcoholic German beer that Karp drinks (he would crack one open on the way out of the park). The case contained a wooden sword, which he needed for the next part of his routine. "I brought a real sword the last time I was here, but the police stopped me," he said matter of factly as he began slashing the air with the sword. Those gendarmes evidently didn't know that Karp, far from being a public menace, was the chief executive of an American company whose software has been deployed on behalf of public safety in France. The company, Palantir Technologies, is named after the seeing stones in J.R.R. Tolkien's "The Lord of the Rings." Its two primary software programs, Gotham and Foundry, gather and process vast quantities of data in order to identify connections, patterns and trends that might elude human analysts. The stated goal of all this "data integration" is to help organizations make better decisions, and many of Palantir's customers consider its technology to be transformative. Karp claims a loftier ambition, however. "We built our company to support the West," he says. To that end, Palantir says it does not do business in countries that it considers adversarial to the U.S. and its allies, namely China and Russia. In the company's early days, Palantir employees, invoking Tolkien, described their mission as "saving the shire." The brainchild of Karp's friend and law-school classmate Peter Thiel, Palantir was founded in 2003. It was seeded in part by In-Q-Tel, the C.I.A.'s venture-capital arm, and the C.I.A. remains a client. Palantir's technology is rumored to have been used to track down Osama bin Laden -- a claim that has never been verified but one that has conferred an enduring mystique on the company. These days, Palantir is used for counterterrorism by a number of Western governments.
Sinai School of Medicine, Stanford University and the Northern California Institute for Research and Education, IBM Research is undertaking a new research initiative funded by the National Institutes of Health. As part of a broader $99 million, 5-year research initiative spanning multiple public and private organizations and research institutions, this work will tap into AI and big data to help better identify individuals at high-risk of developing schizophrenia, a serious mental illness affecting how a person thinks, feels and behaves. Schizophrenia is often characterized by alterations to a person's thoughts, feelings and behaviors, which can include a loss of contact with reality known as psychosis. A better understanding of how this disease could be detected prior to psychosis could help to postpone or even prevent the transition to psychosis, as well as possibly improve outcomes. The project is a component of the Accelerating Medicines Partnership (AMP), a collaboration between the National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), pharmaceutical companies, biotech firms and nonprofit organizations.
That's key, since inconvenience is the enemy of sales. The pandemic wreaked havoc on supply chains, which, coupled with consumer reluctance to buy nondiscretionary items, reduced data earlier this year. Retailers that could afford AI could adjust, often by tapping nontraditional data. "Mobile is the new mall," says Cowen analyst Oliver Chen, who notes that machine learning allows brands to build one-on-one relationships with consumers at scale. That's part of the rationale behind Walmart's bid for TikTok, which provides data on how younger shoppers engage with brands via social media.
Scopio Labs, a leading provider of Full Field Morphology (FFM), announced that it was granted FDA clearance to market and sell its X100 with Full Field Peripheral Blood Smear (Full Field PBS) Application, unlocking the potential of in vitro hematology diagnosis. Full Field PBS is also available in Europe with CE mark certification granted earlier this year. Blood is one of the most foundational gateways to health information. Even with the adoption of digital tools, today's solutions do not showcase all required regions of interest in a PBS slide, only capturing snapshots of cells. To help improve diagnostic accuracy leveraging novel computer vision tools, Full Field PBS gives clinical laboratories an unprecedented ability to capture digital scans using advanced computational photography imaging and tailored AI tools.
The U.S. Food and Drug Administration on Thursday convened a public meeting of its Patient Engagement Advisory Committee to discuss issues regarding artificial intelligence and machine learning in medical devices. "Devices using AI and ML technology will transform healthcare delivery by increasing efficiency in key processes in the treatment of patients," said Dr. Paul Conway, PEAC chair and chair of policy and global affairs of the American Association of Kidney Patients. As Conway and others noted during the panel, AI and ML systems may have algorithmic biases and lack transparency – potentially leading, in turn, to an undermining of patient trust in devices. Medical device innovation has already ramped up in response to the COVID-19 crisis, with Center for Devices and Radiological Health Director Dr. Jeff Shuren noting that 562 medical devices have already been granted emergency use authorization by the FDA. It's imperative, said Shuren, that patients' needs be considered as part of the creation process.
Google and startups like Qure.ai, Aidoc, and DarwinAI are developing AI and machine learning systems that classify chest X-rays to help identify conditions like fractures, collapsed lungs, and fractures. Several hospitals including Mount Sinai have piloted computer vision algorithms that analyze scans from patients with the novel coronavirus. But research from the University of Toronto, the Vector Institute, and MIT reveals that chest X-ray datasets used to train diagnostic models exhibit imbalance, biasing them against certain gender, socioeconomic, and racial groups. Partly due to a reticence to release code, datasets, and techniques, much of the data used today to train AI algorithms for diagnosing diseases may perpetuate inequalities. A team of U.K. scientists found that almost all eye disease datasets come from patients in North America, Europe, and China, meaning eye disease-diagnosing algorithms are less certain to work well for racial groups from underrepresented countries.
Aidoc announced today that the US Food and Drug Administration (FDA) has given regulatory clearance for the commercial use of its triaging and notification algorithms for flagging and communicating incidental pulmonary embolism . Flagging incidental, critical findings is a huge technical challenge due to the varied imaging protocols used and lower incidences of such cases. The ability to prioritize incidental critical conditions accurately is a breakthrough in the value AI can bring to the radiologist workflow. "The most common use case we experienced is for critical unsuspected findings in oncology surveillance patients" said Dr. Cindy Kallman, Chief, Section of CT at Cedars-Sinai Medical Center. "The ability to call the referring physician while the patient is still in the house is huge. We are essentially offering a point-of-care diagnosis of PE for our outpatients. Our referring physicians have been completely wowed by this."
These risk estimates are from the World Economic Forum, the Intergovernmental Panel on Climate Change, the Chicago Actuarial Association, the Global Challenges Foundation, Bethan Harris at the University of Reading, and David Morrison at NASA, with advice from Phil Torres at the Institute for Ethics and Emerging Technologies, author of Human Extinction: A Short History. Fully autonomous weapons don't exist yet, but advances in drone technology and AI make them likely. Rogue code and irresponsible use could lead to mass violence on a scale and speed we don't understand today. Hacking the transport system or a central bank would wreak havoc and threaten public safety. Prevention relies on educating people about cybersecurity.
Summary: A new computational model predicts how information deep inside the brain could flow from one network to another, and how neural network clusters can self optimize over time. Researchers at the Cyber-Physical Systems Group at the USC Viterbi School of Engineering, in conjunction with the University of Illinois at Urbana-Champaign, have developed a new model of how information deep in the brain could flow from one network to another and how these neuronal network clusters self-optimize over time. Their work, chronicled in the paper "Network Science Characteristics of Brain-Derived Neuronal Cultures Deciphered From Quantitative Phase Imaging Data," is believed to be the first study to observe this self-optimization phenomenon in in vitro neuronal networks, and counters existing models. Their findings can open new research directions for biologically inspired artificial intelligence, detection of brain cancer and diagnosis and may contribute to or inspire new Parkinson's treatment strategies. The team examined the structure and evolution of neuronal networks in the brains of mice and rats in order to identify the connectivity patterns.