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Brossard, Quebec, March 25, 2020 (GLOBE NEWSWIRE) -- DIAGNOS Inc. (DIAGNOS or the Corporation) (TSX Venture: ADK) (OTCQB: DGNOF), a leader in early detection of critical health issues through the use of Artificial Intelligence (AI), announces that it is participating in the Call to Action initiative implemented by the White House Office of Science and Technology Policy. DIAGNOS has accessed a significant dataset with the objective of analysing these medical documents with its AI Medical Platform, called FLAIRE, in order to identify key factors that could assist in the battle against the Coronavirus.

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 44,000 scholarly articles, including over 29,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. There is a growing urgency for these approaches because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up.

Call to Action (from the White House)

The White House is issuing a call to action to the world's artificial intelligence experts to develop text and data mining tools that can help the medical community develop answers to high priority scientific questions. The CORD-19 dataset represents the most extensive machine-readable coronavirus literature collection available for data mining to date. This allows the worldwide AI research community the opportunity to apply text and data mining approaches to find answers to questions within, and connect insights across, this content in support of the ongoing COVID-19 response efforts worldwide.

Mr. Andr Larente, CEO of Diagnos stated: Diagnos AI platform has been built to address the complexity of multiple sources of data that includes text mining, data mining and medical imaging. The proprietary technology has been developed over a number of years in order to identify medical challenges, for example cardio vascular issues have resulted in new tests to address hypertensive patient complications and to predict a potential stroke. The intention of FLAIRE in response to the White House Call to Action is to assist in resolving some of the issues caused by the virus by analyzing the dataset made available by the US authorities.

The Standing Committee on Emerging Infectious Disease and 21st Century Health Threats of the US and the WHO identified 10 scientific questions that are vital to address this international crisis. These questions include studying the transmission and incubation of the virus, risk factors for getting the COVID-19, the origin of the virus, and the proper medical practice for treating this disease.

Mr. Francis Bellido, PhD in Medical Microbiology and board member at Diagnos added: One outcome that is particularly remarkable in the COVID-19 crisis is that the majority of the deceased victims had one or several pre-condition(s) before the infection struck such as hypertension, diabetes, obesity or other Cardio Vascular issues, which are the sweet spots for the Diagnos diagnostic assisted platform. We believe that this virus could further alter the cartography of the retina in such patients, and if confirmed, creating the possibility of an additional facet to our existing detection tool for our existing patients.

Dr. Hadi Chakor, Chief Medical Officer at Diagnos added: One of the treatments for COVID-19 is the use of chloroquine or hydroxychloroquine. The recommendations of the American Academy of Ophthalmology on the screening of chloroquine (CQ) and hydroxychloroquine (HCQ) are very clear after taking high doses and for a long period of use, a rigorous follow-up with patients is required. These conditions represent the most severe risks of developing morphological alterations in the retina after treatment with chloroquine. Also, previous studies demonstrate clearly that chloroquine disrupts lysosomal function in retinal neurons and RPE. Modern screening should be based on primary AI-based automatic screening tests to assess the fundus plus optical spectral coherence tomography (SD OCT) exams. These investigations should look beyond the central macula to provide objective screening and to detect subtle changes on the retinal membrane.

The Corporation is also announcing a correction to its press release dated March 9th, 2020: The number of common shares that Mr. Tristram Coffin would hold assuming the exercise of stock warrants should read 11,047,561 instead of 10,624,560.About DIAGNOS

DIAGNOS is a publicly-traded Canadian corporation with a mission of early detection of critical health issues through the use of its Artificial Intelligence (AI) platform FLAIRE. Diagnos can build application rapidly using the FLAIRE platform such as CARA (Computer Assisted Retina Analysis). CARA is an application that integrates with existing equipment (hardware and software) and processes at the point of care. CARAs Artificial Intelligence image enhancement algorithms make standard retinal images sharper, clearer and easier to read. CARA is a cost-effective tool for screening large numbers of patients in real-time and has been cleared for commercialization by several regulatory authorities such as Health Canada, the U.S. Food and Drug Administration, European Union and other countries.

Additional information is available at http://www.diagnos.com and http://www.sedar.com.

This news release contains forward-looking information. There can be no assurance that forward-looking information will prove to be accurate, as actual results and future events could differ materially from those anticipated in these statements. DIAGNOS disclaims any intention or obligation to publically update or revise any forward-looking information, whether as a result of new information, future events or otherwise. The forward-looking information contained in this news release is expressly qualified by this cautionary statement.

Neither the TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this release.

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DIAGNOS Will Utilize its Artificial Intelligence Medical Platform FLAIRE in Response to the US White House - Call to Action to Analyse and Transform...

The Spanish government is planning to test 80,000 people a day for coronavirus with the roll-out of robot testers.

Technology will be used to speed up testing of people in Spain, one of the countries hardest hit by the Covid-19 outbreak, with more than 200 deaths so far. According to Bloomberg, Spanish authorities now plan to increase daily testing from about 20,000 a day to 80,000, by using four robots to apply artificial intelligence (AI) to testing.

Speaking at a conference on Saturday 21 March, Raquel Yotti, head of Madrids health institute, said: A plan to automate tests through robots has already been designed and Spain has committed to buying four robots that will allow us to execute 80,000 tests per day.

Because of the ease that coronavirus spreads from person to person, testing has been identified as one of the best ways to control the disease. But testing has cost and resource limitations. Applying AI and robot technology could help overcome these problems, while reducing medical practitioners exposure to the virus.

No further details have been given about how the robots will work, but AI is increasingly being designed to work in the healthcare industry by automating some of the work of medical staff, giving them more time to treat patients.

The technology has proved successful in medical trials, including identifying cancer in breast scans.

A research paper from Google Health, published inNaturemagazine, has reported that machine learning, based on Googles TensorFlow algorithm, can be used to reduce false positives in breast cancer scans. A false positive is when a mammogram scan is incorrectly identified as cancerous, and a false negative is when it is wrongly diagnosed as not being cancerous.

In the Google Health paper, based on training an AI algorithm to identify breast cancer using a large representativedataset from the UK and the US, the researchers reported an absolute reduction of 5.7% in false positives in the US dataset, while the UK dataset showed a 1.2% reduction in false positives.

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Coronavirus: Spain to use artificial intelligence to automate testing - ComputerWeekly.com

ATLANTA, March 25, 2020 (GLOBE NEWSWIRE) -- As many government and private institutions scramble to react to the unexpected COVID-19 pandemic, one telehealth company has been prepared for years to respond to this kind of crisis. LiveMD, a leading provider of global telehealth services, has a reliable, established, full-featured telehealth platform that allows anyone to use their phone to check their symptoms, track and self-report viruses such as COVID-19, and talk to a doctor. Plus it offers a verifiable track record of successful service delivery to patients in more than 42 countries worldwide. The company leverages the expertise of doctors in 53 distinct specialties, who are based in 30 different countries.

LiveMD offers an innovative app that can be downloaded directly from the Google Play store at http://bitly.com/livemdapp. With the LiveMD app, anyone can track the COVID-19 corona virus in their local area, regardless of where they live. They can self-report their corona virus status for tracking and covid-19 testing triaging, and connect with local government and medical agencies (such as test labs) for help and guidance. Their personal health information is kept private and secured.

As explained by LiveMD Founder and CEO Emeka Okwara, LiveMD is a global telehealth platform intended to serve anyone on this planet who has a phone. You can think of LiveMD as a global digital hospital on your phone. Anytime and anywhere, virtually anyone can be quickly and safely connected to a certified physician for a live consultation. Anyone can schedule an appointment to talk to a doctor by phone, video, text, or in person. This is our core mission and what we do best.

LiveMD also offers an innovative app that can be securely downloaded directly from the Google Play store. Use the app to talk to a certified doctor in the LiveMD global health network by phone, video, or text from anywhere. With the app, the patient literally has a self-diagnosis tool powered by advanced artificial intelligence capability, in the palm of their hand. After they use the LiveMD app to perform a self-diagnosis, the app then identifies doctors who are available for a consultation and who specialize in that patients specific medical ailment. In the near future, LiveMD will also identify which pharmaceuticals are designed and routinely prescribed to treat that condition.

LiveMD has always advocated for the idea that health care is global and not just local, and must be treated as such. We have led the industry, says Okwara, using our technology with that mindset. This allows us to provide a platform that addresses some of the global health challenges we are experiencing today, and prevent future pandemics.

Organizations such as health insurance companies, business owners and employers, and NGOs can use LiveMD to serve employees and customers. That ensures that those who get the app will receive vital access to the help they need, supported by a global network of doctors.

Okwara adds that, "LiveMD plans to work with governments and provide them with the tools to get real-time COVID-19 updates from their citizens, help triage individuals for covid-19 testing, and help provide citizens the help they desperately need. Our objective is to help governments quickly and efficiently reduce the spread of the virus and save lives. Government institutions can reach LiveMD at gov@mylivemd.com to quickly get onboard the LiveMD Global Health Platform. It takes less than an hour to get onboarded. Because of LiveMDs depth of experience and technological sophistication, the platform can swiftly and strategically respond to individual, local, regional, and global health concerns.

About LiveMD

LiveMD is a Telehealth platform used to increase access to quality healthcare services around the world using its artificial intelligence, big data and telecommunication technologies. Patients across 43 countries use LiveMD to talk to certified doctors by phone, video, and text. They also use LiveMDs artificial intelligence tools for self-diagnosis, virus tracking andmedical testing triaging.You can find more information about LiveMD at http://www.mylivemd.com and you can download the app at http://bitly.com/livemdapp.

LiveMD Media Contact:Emeka Okwarapress@mylivemd.com

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LiveMD Global Telehealth Platform launches artificial intelligence tracking and triaging tools to help combat COVID-19 (CoronaVirus) Pandemic -...

Fraud mitigation is one of the most sought-after artificial intelligence (AI) services because it can provide an immediate return on investment. Already, many companies are experiencing lucrative profits thanks to AI and machine learning (ML) systems that detect and prevent fraud in real-time.

According to a new report, Highmark Inc.s Financial Investigations and Provider Review (FIPR) department generated $260 million in savings that would have otherwise been lost to fraud, waste, and abuse in 2019. In the last five years, the company saved $850 million.

We know the overwhelming majority of providers do the right thing. But we also know year after year millions of health care dollars are lost to fraud, waste and abuse, said Melissa Anderson, executive vice president and chief audit and compliance officer, Highmark Health. By using technology and working with other Blue Plans and law enforcement, we have continually evolved our processes and are proud to be among the best nationally.

FIPR detects fraud across its clients services with the help of an internal team made up of investigators, accountants, and programmers, as well as seasoned professionals with an eye for unusual activity such as registered nurses and former law enforcement agents. Human audits performed to detect unusual claims and assess the appropriateness of provider payments are used as training data for AI systems, which can adapt and react more rapidly to suspicious changing consumer behavior.

As fraudulent actors have become increasingly aggressive and cunning with their tactics, organizations are looking to AI to mitigate rising threats.

We know it is much easier to stop these bad actors before the money goes out the door then pay and have to chase them, said Kurt Spear, vice president of financial investigations at Highmark Inc.

Elsewhere, Teradata, an AI firm specialized in selling fraud detection solutions to banks, claims in a case study that it helped Danske Bank reduce its false positives by 60% and increased real fraud detection by 50%.

Other service operators are looking to AI fraud detection with a keen eye, especially in the health care sector. A recent survey performed by Optum found that 43% of health industry leaders said they strongly agree that AI will become an integral part of detecting telehealth fraud, waste, or abuse in reimbursement.

In fact, AI spending is growing tremendously with total operating spending set to reach $15 billion by 2024, the most sought-after solutions being network optimization and fraud mitigation. According to theAssociation of Certified Fraud Examiners (ACFE)inauguralAnti-Fraud Technology Benchmarking Report,the amount organizations are expected to spend on AI and machine learning to reduce online fraud is expected to triple by 2021.

Mitigating fraud in healthcare would be a boon for an industry that is plagued with many structural inefficiencies.

The United States spends about $3.5 trillion on healthcare-related services every year. This staggering sum corresponds to about 18% of the countrys GDP and is more than twice the average among developed countries. However, despite this tremendous spending, healthcare service quality is lacking. According to a now-famous 2017 study, the U.S. has fewer hospital beds and doctors per capita than any other developed country.

A 2019 study found that the countrys healthcare system is incredibly inefficient, burning through roughly 25% of all its finances which basically go to waste thats $760 billion annually in the best case scenario and up to $935 billion annually.

Most money is being wasted due to unnecessary administrative complexity, including billing and coding waste this alone is responsible for $265.6 billion annually. Drug pricing is another major source of waste, account for around $240 billion. Finally, over-treatment and failure of care delivery incurred another $300 billion in wasted costs.

And even these astronomical costs may be underestimated. According to management firm Numerof and Associates, the 25% waste estimate might be conservative. Instead, the firm believes that as much as 40% of the countrys healthcare spending is wasted, mostly due to administrative complexity. The firm adds that fraud and abuse account for roughly 8% of waste in healthcare.

Most cases of fraud in the healthcare sector are committed by organized crime groups and a fraction of some healthcare providers that are dishonest.

According to the National Healthcare Anti-Fraud Association, the most common types of healthcare frauds in the United States are:

Traditionally, the most prevalent method for fraud management has been human-generated rule sets. To this day, this is the most common practice but thanks to a quantum leap in computing and Big Data, AI-based solutions based on machine learning algorithms are becoming increasingly appealing and most importantly practical.

But what is machine learning anyway? Machine learning refers to algorithms that are designed learn like humans do and continuously tweak this learning process over time without human supervision. The algorithms output accuracy can be improved continuously by feeding them data and information in the form of observations and real-world interactions.

In other words, machine learning is the science of getting computers to act without being explicitly programmed.

There are all sorts of various machine learning algorithms, depending on the requirements of each situation and industry. Hundreds of new machine learning algorithms are published on a daily basis. Theyre typically grouped by:

In a healthcare fraud analytics context, machine learning eliminates the use of preprogrammed rule sets even those of phenomenal complexity.

Machine learning enables companies to efficiently determine what transactions or set of behaviors are most likely to be fraudulent, while reducing false positives.

In an industry where there can be billions of different transactions on a daily basis, AI-based analytics can be an amazing fit thanks to their ability to automatically discover patterns across large volumes of data.

The process itself can be complex since the algorithms have to interpret patterns in the data and apply data science in real-time in order to distinguish between normal behavior and abnormal behavior.

This can be a problem since an improper understanding of how AI works and fraud-specific data science techniques can lead you to develop algorithms that essentially learn to do the wrong things. Just like people can learn bad habits, so too can a poorly designed machine learning model.

In order for online fraud detection based on AI technology to succeed, these platforms need to check three very important boxes.

First, supervised machine learning algorithms have to be trained and fine-tuned based on decades worth of transaction data to keep false positives to a minimum and improve reaction time. This is harder said than done because the data needs to be structured and properly labeled depending on the size of the project, this could take staff even years to solve.

Secondly, unsupervised machine learning needs to keep up with increasingly sophisticated forms of online fraud. After all, AI is used by both auditors and fraudsters. And, finally, for AI fraud detection platforms to scale, they require a large-scale, universal data network of activity (i.e. transactions, filed documents, etc) to scale the ML algorithms and improve the accuracy of fraud detection scores.

According to a new market research report released earlier this year, the healthcare fraud analytics market is projected to reach $4.6 billion by 2025 from $1.2 billion in 2020.

This growth is attributed to more numerous and complex fraudulent activity in the healthcare sector.

In order to tackle rising healthcare fraud, companies offer various analytics solutions that flag fraudulent activity some are rule-based models, but AI-based technologies are expected to form the backbone of all types of analytics used in the future. These include descriptive, predictive, and prescriptive analytics.

Some of the most important companies operating today in the healthcare fraud analytics market include IBM Corporation (US), Optum (US), SAS Institute (US), Change Healthcare (US), EXL Service Holdings (US), Cotiviti (US), Wipro Limited (Wipro) (India), Conduent (US), HCL (India), Canadian Global Information Technology Group (Canada), DXC Technology Company (US), Northrop Grumman Corporation (US), LexisNexis Group (US), and Pondera Solutions (US).

That being said, there is a wide range of options in place today to prevent fraud. However, the evolving landscape of e-commerce and hacking pose new challenges all the time. To keep up, these challenges require innovation that can respond and react rapidly to fraud. The common denominator, from payment fraud to abuse, seems to be machine learning, which can easily scale to meet the demands of big data with far more flexibility than traditional methods.

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Artificial intelligence for fraud detection is bound to save billions - ZME Science

Decision-making based on Artificial Intelligence is on its way to being one of the primary sources of information for the food and beverage industry in the United States and a large part of developed and developing countries.

Artificial Intelligence has revolutionized how the CPG (consumer goods packaged) industry analyzes the data and is used to obtain the best return on your investments when approaching the consumer.

CPG companies must adopt the use of Artificial Intelligence to boost revenue growth through innovation, better forecasts, and better store execution to thrive in todays market.

By 2020 and much of this new decade, businesses will also be equipped with large information banks that will support them in the administration of the Category Management and can identify the areas of opportunity by adding value to the business.

Related Article: Food industry automation gets stronger in 2020

On the other hand, companies dedicated to the production of food and beverages for mass consumption will be strengthened in their interior, creating teams capable of generating updated data that can solve any hypotheses that may arise.

With the use of Artificial Intelligence, they can create strategies that can be translated as action plans that become the driver of the company. Understanding the demand is crucial for the growth of the company, and knowing how the consumer perceives a product is essential to understand the market demand.

The valuable thing about syndicated data and transformed into some indicator is that it has no feelings. This will, therefore, be very useful for making objective decisions.

Packaging, for example, will continue to be a trend and will keep with innovation due to the analysis of data obtained by artificial intelligence. Companies may design according to the real needs and tastes of customers and consumers.

Consumers will raise their expectations at all levels. They will want convenient and personalized products and services, speed, as well as applications capable of interpreting what they want with high precision.

As more CPG companies adopt AI, their tools will become smarter, and by extension, their users, which will result in growth and efficiency amid a turbulent market. The future belongs to companies that combine the promise of Big Data and AI with the power of human intelligence.

Training and the creation of teams that can interpret this data and turn it into better products and services will be necessary. The trend is already there, you add or add yourself.

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Artificial Intelligence In The CPG Industry - Abasto, Hispanic Food and Beverage Industry News