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Digital health helping cancer diagnosis - Pharmaphorum

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The FDA has been championing digital health of late with wide-ranging guidance that derives from the 21st Century Cures Act. This legislation acknowledges the potential that digital health has to make a difference in patient care, potentially leading to more precise therapies. Several developments this week show that the regulator is right to be excited about its potential. Some of the most exciting advances have come in the field of cancer – medical devices firm Angle has produced a new analysis showing that its liquid biopsy device Parsortix could be used instead of conventional tissue biopsies. Parsortix works by monitoring a patient's bloodstream for circulating cancer cells and the University of Southern California research adds to the body of evidence showing that liquid biopsies could replace invasive and unpleasant tissue biopsies in the future.


Artificial intelligence promising for CA, retinopathy diagnoses

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Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, M.D., Ph.D., from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images. The researchers found that the AUC of the DLS for referable diabetic retinopathy was 0.936, and sensitivity and specificity were 90.5 and 91.6 percent, respectively.


Accuracy of Artificial Intelligence Assessed in CA Diagnosis

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A deep learning algorithm can detect metastases in sections of lymph nodes from women with breast cancer; and a deep learning system (DLS) has high sensitivity and specificity for identifying diabetic retinopathy, according to two studies published online December 12 in the Journal of the American Medical Association. Babak Ehteshami Bejnordi, from the Radboud University Medical Center in Nijmegen, Netherlands, and colleagues compared the performance of automated deep learning algorithms for detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer with pathologists' diagnoses in a diagnostic setting. The researchers found that the area under the receiver operating characteristic curve (AUC) ranged from 0.556 to 0.994 for the algorithms. The lesion-level, true-positive fraction achieved for the top-performing algorithm was comparable to that of the pathologist without a time constraint at a mean of 0.0125 false-positives per normal whole-slide image. Daniel Shu Wei Ting, MD, PhD, from the Singapore National Eye Center, and colleagues assessed the performance of a DLS for detecting referable diabetic retinopathy and related eye diseases using 494,661 retinal images.


People who are good at video games are more intelligent

Daily Mail

Two popular video games act like IQ tests, with the most intelligent players gaining the highest scores, research has shown. Both games, League of Legends and Defence of the Ancients 2 (DOTA 2) involve chess-like strategic thinking. Scientists discovered that high levels of skill in both games correlated with having a high IQ. A similar association has been seen between IQ and chess performance. Two popular video games act like IQ tests, with the most intelligent players gaining the highest scores, research has shown.


The rise of conscious AI is just decades away

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Today, many of the world's leading companies are in a one-of-a-kind race: To bring artificial intelligence (AI) to life. Already, machine learning systems are the core of many businesses, so it's no surprise that updates about this AI or that neural net often pop up on our newsfeed. Such headlines typically read along the lines of, "AI beats human players in video game" or "AI mimics human speech" and even sometimes things like "AI detects cancer using machine learning." But just how close are we to having machines with the intelligence of a human--machines that we can talk with and work with like we do any other individual? While all of the aforementioned developments are real, Yann LeCun, Director of AI Research at Facebook and a professor of computer science at NYU, thinks that we may be overestimating the abilities of today's AI, and, thus building up a bit of hype.


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-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).


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).