Training a doctor takes years of grueling work in universities and hospitals. Building a doctor may be as easy as teaching an AI how to read. Artificial intelligence has taken another step towards becoming an integral part of 21st-century medicine. New research out of Guangzhou, China, published February 11th in Nature Medicine Letters, has demonstrated a natural-language processing AI that is capable of out-performing rookie pediatricians in diagnosing common childhood ailments. The massive study examined the electronic health records (EHR) from nearly 600,000 patients over an 18-month period at the Guangzhou Women and Children's Medical Center and then compared AI-generated diagnoses against new assessments from physicians with a range of experience.
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework.
Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information. Learning a good inverse mapping from training data faces severe challenges, including: (i) scarcity of training data; (ii) need for plausible reconstructionsthat are physically feasible; (iii) need for fast reconstruction, especially in real-time applications. We develop a successful system solving all these challenges, using as basic architecture the recurrent application of proximal gradient algorithm. We learn a proximal map that works well with real images based on residual networks. Contraction of the resulting map is analyzed, and incoherence conditions are investigated that drive the convergence of the iterates. Extensive experiments are carried out under different settings: (a) reconstructing abdominal MRI of pediatric patients from highly undersampled Fourier-space data and (b) superresolving natural face images. Our key findings include: 1. a recurrent ResNet with a single residual block unrolled from an iterative algorithm yields an effective proximal which accurately reveals MR image details. 2. Our architecture significantly outperforms conventional non-recurrent deep ResNets by 2dB SNR; it is also trained much more rapidly.
Prematurity is a significant cause of mortality in neonates. Knowledge of an infant's gestational age is critical in post-delivery treatment plans to reduce neonatal deaths. In high-income countries, prenatal ultrasound scans – the ground truth measure – are the gold-standard method to track gestational aging, but in lower-income countries, access to ultrasound technology and medical experts is limited. The cross-disciplinary team, led by Sina Farsiu, has developed a system based on the previously reported inverse correlation between blood vessel density in the anterior lens capsule region, and gestational age. In this new study, an ophthalmoscope has been attached to a handheld, smartphone-based device to take videos of the ALCV of 124 premature neonates in their first 48 hours of life.
Several years ago Vinod Khosla, the Silicon Valley investor, wrote a provocative article titled "Do We Need Doctors or Algorithms?" Khosla argued that doctors were no match for artificial intelligence. Doctors banter with patients, gather a few symptoms, hunt around the body for clues, and send the patient off with a prescription. This sometimes (accidentally, maybe) leads to the correct treatment, but doctors are acting on only a fraction of the available information. An algorithm, he wrote, could do better. I'm a pediatric and adolescent physician in the San Francisco Bay Area, where entrepreneurs like Khosla have been knocking on the doors of doctors for years with their pilot technologies and software and hardware.
Your are right to highlight councils' use of data about adults and children without their permission, alongside the warped stereotypes that inevitably shape the way families are categorised (Council algorithms use family data to predict child-abuse risk, 17 September). But the problems are more wide-ranging. In policy debates shaped by the Climbié and Baby P scandals, pre-emptive interventions sound attractive, but ethical debates about what level of intervention in family life, on what basis, and how pre-emptively, still need to take place. Such debates would be necessary with accurate predictions but become absolutely crucial when, as with any risk screening programme, false positives are unavoidable. In a given population where the base rate of abuse is low, these errors will be drastically higher than commonly believed.
Preterm newborns undergo various stresses that may materialize as learning problems at school-age. Sleep staging of the Electroencephalogram (EEG), followed by prediction of their brain-age from these sleep states can quantify deviations from normal brain development early (when compared to the known age). Current automation of this approach relies on explicit sleep state classification, optimizing algorithms using clinician visually labelled sleep stages, which remains a subjective gold-standard. Such models fail to perform consistently over a wide age range and impacts the subsequent brain-age estimates that could prevent identification of subtler developmental deviations. We introduce a Bayesian Network utilizing multiple Gaussian Mixture Models, as a novel, unified approach for directly estimating brain-age, simultaneously modelling for both age and sleep dependencies on the EEG, to improve the accuracy of prediction over a wider age range.
Google has developed an AI tool to help flag child sex abuse content online. The free tool uses image recognition to help human moderators spot and remove child sexual abuse material (CSAM) more quickly. It will reduce moderators' exposure to content that can be traumatic, while hopefully catching greater quantities of child sex abuse content. The move comes as UK officials have called on Google and other Silicon Valley giants to take greater action against online child sexual abuse. Google has developed an AI tool to help flag child sex abuse content online.
This is new territory for families. For the first time, children who are too young to distinguish fantasy from reality are engaging with devices powered by artificial intelligence. Many see smart speakers as magical, imbue them with human traits and boss them around like a Marine drill instructor, according to several new studies in the past year. Hunter Walk, a San Francisco venture capitalist, worried that his family's Amazon Echo "is turning our daughter into a raging asshole," he wrote in a blog post in 2016, because of the 4-year-old's tendency to boss it around. He has since set rules around how to talk to the device and said he hasn't noticed any rude behavior by his daughter, who is now 6. "I still have concerns," Mr. Walk says.
Facebook gives special protections to Tommy Robinson and allows people to racially abuse immigrants, according to a new report. Graphic images and videos of children, violent hate speech and racist content are not immediately or automatically removed from the site, according to footage taken by Channel 4's Dispatches. An undercover reporter filmed the people who review content to decide whether it should stay up to be viewed by the public, gaining an unprecedented insight into what is allowed to be posted on the platform. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.