Pediatrics/Neonatology


Saykara Launches First Fully Ambient AI Healthcare Voice Assistant

#artificialintelligence

Saykara today announced the release of Kara 2.0, an AI-powered healthcare assistant that further simplifies the documentation process for physicians. Now featuring Ambient Mode, Kara 2.0 is a breakthrough AI-powered voice application for healthcare, allowing physicians and patients to interact as they normally do, all while Saykara listens, transcribes to text, parses text into structured data, and intelligently completes each form in a patient's electronic health record (EHR or chart). Saykara then automatically generates a clinic note including patient history, physical, assessment, plan, orders and referrals. With the release of Ambient Mode, Saykara is the only virtual healthcare assistant that can be used passively'in the room' during physician-patient appointments with no voice commands. Ambient Mode builds on Saykara's versatility and agnosticity, allowing it to better serve up to 18 disparate healthcare specialties, including primary care, pediatrics, internal medicine, orthopedics, urology and more.


How AI and Neuroscience can save our children's education, health and well being.

#artificialintelligence

AI can be our worst enemy or our best friend, but we do have a choice. Humans have a bias towards fear induced decisions, what do I mean by that? Our brains have evolved over millions of years, but have only been hit with technology in the past hundred years or so. Our brains are still geared to preservation, sexual reproduction and just plain survival. To paraphrase the words of the famous physicist Niels Bohr who was referring to quantum physics'If you are not shocked by the prospect of AI and its effect, on the human race you have not understood it.'


Chinese pre-schools use robots to do daily health checks of children

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The school nurse of the future could be a robot if Chinese technology catches on – but British people may be too suspicious, experts say. Children at more than 2,000 pre-schools in the Asian country now have their health checked every morning by a machine. The Walklake robot, which has a square body and cartoon-like face, takes just three seconds to scan a child's hands, eyes, and throats. And if it picks up any signs of illness – red eyes, rashes or mouth ulcers, for example – it can refer the child to a human nurse. One British doctor said he thought parents in the UK wouldn't want the technology and it could disrupt children's learning, but another called it'a great idea'.


Chinese pre-schools use robots to do daily health checks of children

#artificialintelligence

The school nurse of the future could be a robot if Chinese technology catches on – but British people may be too suspicious, experts say. Children at more than 2,000 pre-schools in the Asian country now have their health checked every morning by a machine. The Walklake robot, which has a square body and cartoon-like face, takes just three seconds to scan a child's hands, eyes, and throats. And if it picks up any signs of illness – red eyes, rashes or mouth ulcers, for example – it can refer the child to a human nurse. One British doctor said he thought parents in the UK wouldn't want the technology and it could disrupt children's learning, but another called it'a great idea'.


How Wadhwani brothers Sunil and Romesh are using AI to serve the underserved

#artificialintelligence

Artificial intelligence (AI) is the 21st century space race where India lags far behind leaders like China and the US. However, there is one area where the country, with second largest number of poor, can lead the world. It can use AI to solve problems for the underserved billions. That's exactly what Wadhwani Institute of AI does. Launched last February by prime minister Narendra Modi, backed by NRI entrepreneurs (Rs 200 crore grant) - Wadhwani brothers Sunil and Romesh – WIAI is using AI to serve the bottom of the pyramid.


The Pediatric AI That Outperformed Junior Doctors

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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.


Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence

#artificialintelligence

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.


How to set screen time limits for your children on iPhones, Android, computers, Instagram and YouTube

The Independent - Tech

Parents and experts are increasingly concerned about the damage being done to children by spending too much time looking at screens. The latest warning comes from the Royal College of Paediatrics and Child Health, which suggested that excessive use of screens could bring a whole host of negative outcomes for young people. That includes everything from bad sleep to the potential for cyber bullying, though the organisation warned that the damage might be overestimated. Helpfully, the technology industry is increasingly aware of the same problems and is trying to solve them using products. As concern has grown about the damage their products do, developers have added features that stop other features being used – monitoring how long people spend on their phones, and kicking them off when it gets too much.


Neural Proximal Gradient Descent for Compressive Imaging

Neural Information Processing Systems

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.


Neural Proximal Gradient Descent for Compressive Imaging

Neural Information Processing Systems

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 reconstructions that 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 repetitive application of alternating proximal and data fidelity constraints. We learn a proximal map that works well with real images based on residual networks with recurrent blocks. Extensive experiments are carried out under different settings: (a) reconstructing abdominal MRI of pediatric patients from highly undersampled k-space data and (b) super-resolving natural face images. Our key findings include: 1. a recurrent ResNet with a single residual block (10-fold repetition) 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. 3. It outperforms state-of-the-art compressed-sensing Wavelet-based methods by 4dB SNR, with 100x speedups in reconstruction time.