Picture this: a patient walks into the emergency department and sits in front of the "triage nurse" -- a computer that uses advanced algorithms to ask questions based on the patient's answers. Researchers at the Massachusetts Institute of Technology (MIT) are testing robotic decision supports that schedule nursing tasks and assign rooms to patients. TAVIE uses pre-recorded videos of a nurse to coach patients to manage their health condition and make behaviour changes. Ryan Chan, an emergency nurse and a master's student, is working with Booth and his research team as they develop an online computer game to teach electronic medication administration to nursing students.
The first panel featured our Flying Labs Coordinators from Tanzania (Yussuf), Peru (Juan) and Nepal (Uttam). Last but not least, Uttam showed how Nepal Flying Labs has been using flying robots for agriculture monitoring, damage assessment and mapping of property rights. He also gave an overview of the social entrepreneurship training and business plan competition recently organized by Nepal Flying Labs. UNICEF (Judith) highlighted the field tests they have been carrying out in Malawi; using cargo robotics to transport HIV samples in order to accelerate HIV testing and thus treatment.
Our partners at the joint FAO/IAEA Insect Pest Control Lab in Vienna, Austria have been working to perfect the Sterile Insect Technique (SIT) in order to sterilize and release male mosquitos in Zika hotspots. Our field tests in Peru will also seek to identify the optimal flight parameters for the targeted and homogenous delivery of sterilized mosquitos. The plan is for Peru Flying Labs to operate the flying robots and release mechanisms as need once we have a more robust version of the release mechanism. The vision here is to have a fleet of flying robots at our Flying Labs equipped with release mechanisms in order to collectively release millions of sterilized mosquitos over relatively large areas.
But the AI's work isn't done yet. Comparing the change in genetic code with infection rates and virulence factors could give us a better model for working toward a vaccine for this insufferable virus. And if we finally managed to program an AI that would tell us how it arrives at its conclusions, that would be a powerful collaboration indeed. Imagine an AI that evolves with the virus it tracks.
This meant that Zipline's drones, which have a flight range of 150 kilometers, could serve nearly half the country from a single launch site. Zipline's plans for Rwanda include scaling up to a wide range of medical products, including emergency rabies vaccines; drugs to treat HIV, tuberculosis, and malaria; contraceptives; and diagnostic test kits. Today, Rwanda's Ministry of Health stores blood at a national center in Kigali and four regional depots around the country; its 58 facilities equipped to handle blood transfusions, mainly hospitals, keep a small inventory of common blood types and must continually restock from the depots or national center. The World Health Organization estimates that Rwanda has one maternal death for every 344 live births, 20 times the rate in the United States and 97 times the rate in the top-performing countries in Europe.
PALO ALTO, Calif., June 06, 2017 --Cloudera, Inc., (NYSE: CLDR), the leading provider of the modern platform for machine learning and advanced analytics built on the latest open source technologies, announced that Inova Translational Medicine Institute (ITMI), a global leading medical research institute, has deployed Cloudera Enterprise to securely analyze massive collections of clinical and genomic data at unprecedented speeds and scale for faster innovations in translational medicine research. As part of the Inova Center for Personalized Health (ICPH), ITMI's team of leading scientists, researchers, analysts and collaborators use machine learning algorithms on terabytes of clinical and genomic information to identify the genetic links to diseases. This genomic data analysis allows a bioinformatics scientist to study genomic correlations from people with conditions like arthritis, autoimmune diseases or cancer. Working with Cloudera, ITMI built a world-class bioinformatics infrastructure for the Institute's massively growing data collection of genomes paired against the clinical record.
One company is using the human immune system as inspiration to develop a security system unlike anything we've seen before. Founded in 2013, London-based startup DarkTrace has taken in $104.5 million in 3 rounds of funding from investors that include Japanese technology conglomerate Softbank. Darktrace is "one of the world's fastest-growing cyber defense companies" and a leader in Enterprise Immune System technology, a new category of cyber solutions based on pioneering Bayesian mathematics developed by the University of Cambridge. Yes this whole "immune system" thing sounds pretty cool, and the name "Darktrace" implies that they're out there shutting down all kinds of sinister boogie men just like James Bond, but does this thing really works?
The Center on Artificial Intelligence for Social Solutions (CAISS) has developed a tool which identifies peer leaders within Los Angeles' homeless community to spread awareness about HIV prevention. The chatbots use natural language processing on Facebook Messenger. In addition, the CC-Cruiser will be able to utilize big data by pooling worldwide cases to improve the AI further. Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue.
Researchers are training artificial intelligence to identify tuberculosis on chest X-rays, an initiative that could help screening and evaluation efforts in TB-prevalent areas lacking access to radiologists. "An artificial intelligence solution that could interpret radiographs for the presence of TB in a cost-effective way could expand the reach of early identification and treatment in developing nations," study co-author Paras Lakhani, MD, from Thomas Jefferson University Hospital in Philadelphia, wrote in the journal. The cases consisted of multiple chest X-ray datasets from the National Institutes of Health, the Belarus Tuberculosis Portal, and Thomas Jefferson University Hospital. In 2016, approximately 10.4 million people fell ill from TB, resulting in 1.8 million deaths.
The researchers obtained 1,007 X-rays of patients with and without active TB, using multiple chest X-ray datasets from the National Institute of Health, the Belarus Tuberculosis Portal and TJUH. The datasets were split into three categories--training (68 percent), validation (17.1 percent) and test (14.9 A pair of DCNN models--AlexNet and GoogLeNet--were used to learn from TB-positive and TB-negative X-rays. For these 13 cases the researchers evaluated a workflow with an expert radiologist who was able to interpret the images and accurately diagnose 100 percent of the cases. "An artificial intelligence solution using chest imaging can play a big role in tackling TB."