Health & Medicine


AI to drive GDP gains of $15.7 trillion with productivity, personalisation improvements

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Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Methodology: To estimate AI impact, our team conducted a dual-phased top-down and bottom-up analysis combining a detailed assessment of the current and future use of AI and an exploration of the economic impact in terms of new jobs, new products, and other secondary effects. Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.


ai-as-a-way-to-overcome-cognitive-bias-in-physicians

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However, a number of cognitive biases can emerge, and can lead clinicians into making erroneous conclusions that are often only seen in retrospect. Diagnosis of the current patient biased by experience with past cases. Conceptually, an Artificial Intelligence (AI) system can overcome these cognitive biases, and deliver personalized, evidence-based rational recommendations in real time to clinicians (and patients) at the point of care. Once the data about the individual is gathered, it is compared to the experience derived from a large base of clinical data in order to match patterns and predict outcomes.


How artificial intelligence can solve worldwide unemployment Access AI

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On the other hand sophisticated robots make it possible to move from mass production into personal production. Already today the internet sector employs a significant number of programmers, designers and marketing people. As we move from one dimensional internet into more dimensional virtual reality employment will increase dramatically. The first implicit assumption behind this thought is that while our technology creates sophisticated machines and software, our education system will not make use of these new opportunities.


Deep-Learning Networks Rival Human Vision

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For most of the past 30 years, computer vision technologies have struggled to help humans with visual tasks, even those as mundane as accurately recognizing faces in photographs. Recent progress in a deep-learning approach known as a convolutional neural network (CNN) is key to the latest strides. Convolutional neural networks do not need to be programmed to recognize specific features in images--for example, the shape and size of an animal's ears. Deep learning for visual tasks is making some of its broadest inroads in medicine, where it can speed experts' interpretation of scans and pathology slides and provide critical information in places that lack professionals trained to read the images--be it for screening, diagnosis, or monitoring of disease progression or response to therapy.


Nao robots could soon help children with autism

Daily Mail

It works by scanning children with an autism spectrum disorder (ASD) for their facial expressions and body movements in certain scenarios. Developed by a French robotic firm, the machine will also function as a diagnostic tool by collecting data in the future. The robot works by scanning children with an autism spectrum disorder (ASD) for their facial expressions and body movements in certain scenarios. Developed by a French robotic firm, the machine will also function as a diagnostic tool by collecting clinical data during therapy.


How AI Is Transforming Drug Creation – The Data Intelligence Connection – Medium

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But samples also were sent to a lab where computers using artificial intelligence are changing the way pharmaceutical companies develop drugs. Biological insights driven by machine learning also could help pharmaceutical companies better identify and recruit patients for clinical trials of therapies most likely to work for them, perhaps boosting the chances of those medications' getting approved by regulatory agencies such as the Food and Drug Administration. Other efforts to leverage AI technology in pharmaceutical research include using it to find new drugs or new uses for already approved medications, as well as speeding up clinical trials by improving patient recruitment and site selection, according to a May 2017 report by analyst Datamonitor Healthcare. Recently, there's been growing interest in leveraging this type of AI for health-care applications, in part due to the vast improvements deep learning has enabled in applications like machine translation and computer vision, which also rely on pattern recognition.


Why AI Should Not Be Compared to Humans in Regard to Decision-Making

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In contrast, most AI methods require very large datasets containing matched pairs of "predictor data" and "criteria data." For example, to model work characteristics causing employee stress, you would need predictor data that measured characteristics that might cause stress, as well as criteria data on these same employees that measured stress levels. Of course, AI is amazingly good at finding treasures of useful information in massive piles of garbage data, but it can't find treasures in data that is entirely composed of garbage. Examples include modeling relationships between applicant characteristics and post-hire retention, job characteristics and employee turnover, and employee work characteristics and absenteeism and healthcare costs.


Aim: Systems, Outcomes and the Future of Healthcare Service Delivery - Artefact

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Personalized services and intelligent assistants help us navigate choices and make informed decisions in the office, in the car and at home. But despite how advancements like machine learning, big data, ubiquitous computing, and universal connectivity have forever changed these everyday interactions, our experiences with healthcare remains stuck in a disconnected, opaque and rigid past. Clinicians often don't have access to useful patient data, which remains siloed within disparate sources. We called the concept Aim as a play on artificial intelligence and medicine, augmented interactions and mobility, but it is also a goal we should aspire to as we imagine, design and build a more integrated, effective and efficient healthcare.


How artificial intelligence will impact the future of healthcare

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"IBM's Watson read 25 million scientific papers in a week." Quartz Magazine reported on an AI called AtomNet that promises to develop new drug treatments for dangerous diseases like Ebola and multiple sclerosis. Computer-assisted coding isn't exactly a form of artificial intelligence, but some in the industry promised that it would improve coder productivity and efficiency using a spell checker-like system called NLP. By auditing all charts prior to billing, eValuator acts as a highly skilled AI auditor, flagging any charts that are likely to have mistakes.


Artificial Intelligence Market Shows Signs of Growth in Healthcare and Finance Sectors - openPR

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Artificial Intelligence Market Global Artificial Intelligence Market: Snapshot Globally, there is a wave of artificial intelligence across various industries, especially consumer electronics and healthcare. According to a study by Transparency Market Research (TMR), the global market for artificial intelligence is estimated to post an impressive 36.1% CAGR between 2016 and 2024, rising to a valuation of US$3,061.35 The upward growth of the market is, however, hampered by the low upfront investments. About Us Transparency Market Research (TMR) is a market intelligence company, providing global business information reports and services. These reports provide in-depth analysis and deep segmentation to possible micro levels.