Please stand in front of Walklake for your examination. This health checking robot takes just 3 seconds to diagnose a variety of ailments in children, including conjunctivitis, and hand, foot and mouth disease. Over 2000 preschools in China, with children aged between 2 and 6, are using Walklake every morning to check the health status of their students. Walklake has a boxy body and smiling cartoony face.
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Inland Empire Health Plan in Rancho Cucamonga, California, is the largest not-for-profit Medi-Cal and Medicare health plan in the Inland Empire, a metropolitan area in Southern California. Comprehensive medication reviews, a significant utilizer of trained clinical pharmacist resources, were not focused on plan members most in need, and the review process was manual and time-consuming. Moreover, it was unclear if member outcomes were improving because of this program. Preveon is a specialty pharmacy focused on chronic disease management. Inland Empire Health Plan has outsourced its medication review process to Preveon for its MyMeds Program, and as such, purchased Surveyor Health licenses and allocated them to Preveon.
Geoffrey Hinton is one of the creators of Deep Learning, a 2019 winner of the Turing Award, and an engineering fellow at Google. Last week, at the company's I/O developer conference, we discussed his early fascination with the brain, and the possibility that computers could be modeled after its neural structure--an idea long dismissed by other scholars as foolhardy. We also discussed consciousness, his future plans, and whether computers should be taught to dream. The conversation has been lightly edited for length and clarity. Nicholas Thompson: Let's start when you write some of your early, very influential papers. Everybody says, "This is a smart idea, but we're not actually going to be able to design computers this way." Explain why you persisted and why you were so confident that you had found something important. Geoffrey Hinton: It seemed to me there's no other way the brain could work. It has to work by learning the strength of connections. And if you want to make a device do something intelligent, you've got two options: You can program it, or it can learn. And people certainly weren't programmed, so we had to learn. This had to be the right way to go. NT: Explain what neural networks are. GH: You have relatively simple processing elements that are very loosely models of neurons. They have connections coming in, each connection has a weight on it, and that weight can be changed through learning. And what a neuron does is take the activities on the connections times the weights, adds them all up, and then decides whether to send an output.
The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patient's race, age, sex, preoperative intraocular pressure, preoperative visual acuity, number of intraocular pressure-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final intraocular pressure greater than 18 mm Hg, reduction in intraocular pressure less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine.
In a recent pilot study, researchers from the National University of Singapore (NUS) have shown that a powerful artificial intelligence (AI) platform known as CURATE.AI could potentially be used to customise training regimens for individuals to personalise learning and improve cognitive performance. Using performance data from a given person, CURATE.AI creates an individualised profile that enables cognitive training to be tailored to the individual's learning habits and competencies so as to enhance training effectiveness. Such dynamic AI-guided personalisation overcomes the current limited improvement produced by using traditional training methods which often involve repetitive behavioural exercises. The results of the study provide evidence that the CURATE.AI platform has the potential to enhance learning, and paves the way for promising applications for personalised digital therapy, including the prevention of cognitive decline. The research, led by Professor Dean Ho and Assistant Professor Christopher L. Asplund from the N.1 Institute for Health (N.1) of NUS, which was formerly the Singapore Institute for Neurotechnology (SINAPSE), was published in the journal Advanced Therapeutics on 22 May 2019.
One of lung cancer's most lethal attributes is its ability to trick radiologists. Some nodules appear threatening but turn out to be false positives. Others escape notice entirely, and then spiral without symptoms into metastatic disease. On Monday, however, Google unveiled an artificial intelligence system that -- in early testing -- demonstrated a remarkable talent for seeing through lung cancer's disguises. A study published in Nature Medicine reported that the algorithm, trained on 42,000 patient CT scans taken during a National Institutes of Health clinical trial, outperformed six radiologists in determining whether patients had cancer.
Measles, once thought to have been eliminated in the U.S., is popping up in isolated outbreaks as a result of skipped well-child visits and parents' fears that the measles-mumps-rubella (MMR) vaccine is linked to autism. Though some 350 measles cases occurred in 15 states in the first three months of 2019, more than half were in Brooklyn, N.Y., and nearby Rockland County, N.Y., where large religious communities have adopted anti-vaccine positions. Rockland County responded by pulling 6,000 unvaccinated children out of schools and barring them from public places. The county's actions were effective; in just a few months, 17,500 doses of MMR were administered to area children. Yet, wouldn't it have been better to contain the outbreak before it got started?
The condition is the leading cause of cancer-related death in the U.S., and early detection is crucial for both stopping the spread of tumors and improving patient outcomes. As an alternative to chest X-rays, healthcare professionals have recently been using computed tomography (CT) scans to screen for lung cancer. In fact, some scientists argue that CT scans are superior to X-rays for lung cancer detection, and research has shown that low-dose CT (LDCT) in particular has reduced lung cancer deaths by 20%. These errors typically delay the diagnosis of lung cancer until the disease has reached an advanced stage when it becomes too difficult to treat. New research may safeguard against these errors.