Results


The Future of Artificial Intelligence and Quality Management

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Leading safety metrics provide: Total number of noncompliances Number of near-misses enabling investigation to prevent potential incidents The time it takes to complete post-audit corrective and preventive actions Easy-to-view previous findings for corrective action launches and findings Automated audit management software that centralizes all risk items and allows users to automatically assess them and generate reports quickly to pinpoint high-risk gaps that may otherwise go unnoticed The same goes for automated employee training tools, which create: Integration of employee data Creation and linking of requirements Integration with document control Integration with adverse events, reporting, and change management Employee training software helps ensure the first step is laid out for proper training. Such automated tools allow managers more proactivity in delivering their work, continually making improvements as the organization's processes and systems grow. This latest technology wave is the engineered product equivalent, based on enabling engineering and development, design-in quality and reliability based on current field data." Whether in the form of an everyday tool like Siri, a new product being delivered to market, or in healthcare to generate early diagnoses or more accurate results, AI is truly an advantage fostering healthy change.


Doctor Hazel, an AI aimed at skin cancer detection, is latest in a long line

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Engineers participating in a hackathon last weekend demonstrated an artificial intelligence that they say could someday detect cancerous moles, TechCrunch reports. Apps, mobile platforms, and camera devices designed to evaluate moles and estimate skin cancer risk have a long history filled with successes and failures. That same year, University of Michigan Health System physicians launched UMSkinCheck featuring reminders and instructions for patients to self-examine their moles and skin lesions over time. The FTC alleged that the marketers of both mole photography-based apps "deceptively claimed the apps accurately analyzed melanoma risk," and that the marketers had insufficient evidence to make these claims.


Intel Invests $1 Billion in the AI Ecosystem to Fuel Adoption and Product Innovation Intel Newsroom

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At Intel, we have an optimistic and pragmatic view of artificial intelligence's (AI) impact on society, jobs and daily life that will mimic other profound transformations – from the industrial to the PC revolutions. To drive AI innovation, Intel is making strategic investments spanning technology, R&D and partnerships with business, government, academia and community groups. We have also invested in startups like Mighty AI*, Data Robot* and Lumiata* through our Intel Capital portfolio and have invested more than $1 billion in companies that are helping to advance artificial intelligence. To support the sheer breadth of future AI workloads, businesses will need unmatched flexibility and infrastructure optimization so that both highly specialized and general purpose AI functions can run alongside other critical business workloads.


Machine Learning: An In-Depth Guide – Model Performance and Error Analysis

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These include: true positives, false positives (type 1 error), true negatives, and false negatives (type 2 error). There are many metrics for determining model performance for regression problems, but the most commonly used metric is known as the mean square error (MSE), or variation called the root mean square error (RMSE), which is calculated by taking the square root of the mean squared error. Recall the different results from a binary classifier, which are true positives, true negatives, false positives, and false negatives. Precision (positive predictive value) is the ratio of true positives to the total amount of positive predictions made (i.e., true or false).


machine-learning-an-in-depth-non-technical-guide-part-4

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These include: true positives, false positives (type 1 error), true negatives, and false negatives (type 2 error). There are many metrics for determining model performance for regression problems, but the most commonly used metric is known as the mean square error (MSE), or variation called the root mean square error (RMSE), which is calculated by taking the square root of the mean squared error. Recall the different results from a binary classifier, which are true positives, true negatives, false positives, and false negatives. Precision (positive predictive value) is the ratio of true positives to the total amount of positive predictions made (i.e., true or false).


Is It Time To Rethink The Scientific Method?

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Big data and machine learning are revolutionizing the science of making things and make it available to the masses. This approach formed the basis for The Cancer Genome Atlas (TCGA), a joint project between NCI and the National Human Genome Research Institute, which began in 2006. Much like The Cancer Genome Atlas, the Materials Genome Initiative (MGI) collects data on thousands of materials. Much like the Internet democratized knowledge -- a teenager with a smartphone today has more access to information than a specialist at a large institution a generation ago -- big data and machine learning will bring scientific understanding to the masses.


Unintended Consequences of Machine Learning in Medicine

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However, comparative studies on the effectiveness of machine learning–based decision support systems (ML-DSS) in medicine are lacking, especially regarding the effects on health outcomes. It also could lead to reduced interest in and decreased ability to perform holistic evaluations of patients, with loss of valuable and irreducible aspects of the human experience such as psychological, relational, social, and organizational issues. Failing to include difficult to represent factors into medical decision making may lead to other similar contextual errors, and overreliance on ML-DSS may enhance the odds of the occurrence of these types of errors when contextual factors cannot be easily integrated. This observer variability is related not only to interpretive deficiencies, but also to an intrinsic ambiguity in the observed phenomena.7 However, the intrinsic uncertainty of medical observations and interpretations that are part of input to "optimize" machine learning models is not usually considered.


Startup touts neuro-stimulation as 'medicine for the brain'

The Japan Times

According to California startup Halo Neuroscience, the device can help improve the performance of athletes, pilots and surgeons, and potentially help rehabilitation for stroke victims. By stimulating the motor cortex, Chao says the Halo device can "extract latent potential" in the brain to improve performance for people who rely on making quick decisions and movements such as athletes. The San Francisco startup has also concluded deals with the San Francisco Giants baseball team and the U.S. Olympic ski team to integrate Halo in training programs. Chao, who trained as a doctor and studied neuroscience at Stanford, previously worked at a startup called Neuro Pace, which uses electrical stimulation to treat epilepsy.


Brain-training game fails test against regular computer games

New Scientist

The thinking is that this should improve a player's memory, attention, focus and multitasking skills. For this study, Kable and his colleagues recruited 128 young healthy adults for a randomised controlled trial. Those who played Lumosity did show improvements in some cognitive skills, such as attention and focus, but so did those who played the other computer games, and the people who played no games at all. The number of people involved in Kable's study was too small to detect any tiny improvements in performance, so it's possible a small effect was missed.


Machine learning mines EHRs to predict heart failure

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"Model performance was most strongly influenced by the diversity of data, basic feature construction and the length of the observation window," wrote Kenny Ng, research staff member in the Center for Computational Health and first author of the study. "In raw form, EHR data are highly diverse, represented by thousands of variants for disease coding, medication orders, laboratory measures, and other data types. The model performed best when window length was below two years, the training data set at least 4,000 patients, data were diverse as possible and data were confined to patients with more than 10 meetings with physicians in two years. First, the approach and methods need to be validated on larger patient data sets from multiple healthcare systems and additional disease targets to better understand the generalizability of the data characteristic impacts on predictive modeling performance," wrote Ng et al.