By:

Dr. Basheer Hawwash, Principal Data Scientist

Amanda Coogan, Risk-Based Monitoring Senior Product Manager

Rhonda Roberts, Senior Data Scientist

Remarque Systems Inc.

Everyone knows the terms machine learning and artificial intelligence. Few can define them, much less explain their inestimable value to clinical trials. So, its not surprising that, despite their ability to minimize risk, improve safety, condense timelines, and save costs, these technology tools are not widely used by the clinical trial industry.

Basheer Hawwash

There are lots of reasons for resistance: It seems complicated. Those who are not statistically savvy may find the thought of algorithms overwhelming. Adopting new technology requires a change in the status quo.

Yet, there are more compelling reasons for adoption especially as the global pandemic has accelerated a trend toward patient-centricity and decentralized trials, and an accompanying need for remote monitoring.

Machine learning vs. artificial intelligence. Whats the difference?

Lets start by understanding what the two terms mean. While many people seem to use them interchangeably, they are distinct: machine learning can be used independently or to inform artificial intelligence; artificial intelligence cannot happen without machine learning.

Machine learning is a series of algorithms that analyze data in various ways. These algorithms search for patterns and trends, which can then be used to make more informed decisions. Supervised machine learning starts with a specific type of data for instance, a particular adverse event. By analyzing the records of all the patients who have had that specific adverse event, the algorithm can predict whether a new patient is also likely to suffer from it. Conversely, unsupervised machine learning applies analysis such as clustering to a group of data; the algorithm sorts the data into groups which researchers can then examine more closely to discern similarities they may not have considered previously.

In either case, artificial intelligence applies those data insights to mimic human problem-solving behavior. Speech recognition, self-driving cars, even forms that auto-populate all exist because of artificial intelligence. In each case, it is the vast amounts of data that have been ingested and analyzed by machine learning that make the artificial intelligence application possible.

Physicians, for instance, can use a combination of machine learning and artificial intelligence to enhance diagnostic abilities. In this way, given a set of data, machine learning tools can analyze images to find patterns of chronic obstructive pulmonary disease (COPD); artificial intelligence may be able to further identify that some patients have idiopathic pulmonary fibrosis (IPF) as well as COPD, something their physicians may neither have thought to look for, nor found unaided.

Amanda Coogan

Now, researchers are harnessing both machine learning and artificial intelligence in their clinical trial work, introducing new efficiencies while enhancing patient safety and trial outcomes.

The case of the missing data

Data is at the core of every clinical trial. If those data are not complete, then researchers are proceeding on false assumptions, which can jeopardize patient safety and even the entire trial.

Traditionally, researchers have guarded against this possibility by doing painstaking manual verification, examining every data point in the electronic data capture system to ensure that it is both accurate and complete. More automated systems may provide reports that researchers can look through but that still requires a lot of human involvement. The reports are static and must be reviewed on an ongoing basis and every review has the potential for human error.

Using machine learning, this process happens continually in the background throughout the trial, automatically notifying researchers when data are missing. This can make a material difference in a trials management and outcomes.

Consider, if you will, a study in which patients are tested for a specific metric every two weeks. Six weeks into the study, 95 percent of the patients show a value for that metric; 5 percent dont. Those values are missing. The system will alert researchers, enabling them to act promptly to remedy the situation. They may be able to contact the patients in the 5 percent and get their values, or they may need to adjust those patients out of the study. The choice is left to the research team but because they have the information in near-real time, they have a choice.

As clinical trials move to new models, with greater decentralization and greater reliance on patient-reported data, missing data may become a larger issue. To counteract that possibility, researchers will need to move away from manual methods and embrace both the ease and accuracy of machine-learning-based systems.

The importance of the outlier

In research studies, not every patient nor even every site reacts the same way. There are patients whose vital signs are off the charts. Sites with results that are too perfect. Outliers.

Rhonda Roberts

Often researchers discover these anomalies deep into the trial, during the process of cleaning the data in preparation for regulatory submission. That may be too late for a patient who is having a serious reaction to a study drug. It also may mean that the patients data are not valid and cannot be included in the end analysis. Caught earlier, there would be the possibility of a course correction. The patient might have been able to stay in the study, to continue to provide data; alternatively, they could be removed promptly along with their associated data.

Again, machine learning simplifies the process. By running an algorithm that continually searches for outliers, those irregularities are instantly identified. Researchers can then quickly drill down to ascertain whether there is an issue and, if so, determine an appropriate response.

Of course, an anomaly doesnt necessarily flag a safety issue. In a recent case, one of the primary endpoints involved a six-minute walk test. One site showed strikingly different results; as it happened, they were using a different measurement gauge, something that would have skewed the study results, but, having been flagged, was easily modified.

In another case, all the patients at a site were rated with maximum quality of life scores and all their blood pressure readings were whole numbers. Machine learning algorithms flagged these results because they varied dramatically from the readings at the other sites. On examination, researchers found that the site was submitting fraudulent reports. While that was disturbing to learn, the knowledge gave the trial team power to act, before the entire study was rendered invalid.

A changing landscape demands a changing approach

As quality management is increasingly focusing on risk-based strategies, harnessing machine learning algorithms simplifies and strengthens the process. Setting parameters based on study endpoints and study-specific risks, machine learning systems can run in the background throughout a study, providing alerts and triggers to help researchers avoid risks.

The need for such risk-based monitoring has accelerated in response to the COVID-19 pandemic. With both researchers and patients unable or unwilling to visit sites, studies have rapidly become decentralized. This has coincided with the emergence and growing importance of patient-centricity and further propelled the rise of remote monitoring. Processes are being forced online. Manual methods are increasingly insufficient and automated methods that incorporate machine learning and artificial intelligence are gaining primacy.

Marrying in-depth statistical thinking with critical analysis

The trend towards electronic systems does not replace either the need for or the value of clinical trial monitors and other research personnel; they are simply able to do their jobs more effectively. A machine-learning-based system runs unique algorithms, each analyzing data in a different way to produce visualizations, alerts, or workflows, which CROs and sponsors can use to improve patient safety and trial efficiency. Each algorithm is tailored to the specific trial, keyed to endpoints, known risks, or other relevant factors. While the algorithms offer guidance, the platform does not make any changes to the data or the trial process; it merely alerts researchers to examine the data and determine whether a flagged value is clinically significant. Trial personnel are relieved of much tedious, reproducible, manual work, and are able to use their qualifications to advance the trial in other meaningful ways.

The imperative to embrace change

Machine learning and artificial intelligence have long been buzzwords in the clinical trial industry yet these technologies have only haltingly been put to use. Its time for that pendulum to swing. We can move more quickly and more precisely than manual data verification, and data cleaning allow. We can work more efficiently if we harness data to drive trial performance rather than simply to prove that the study endpoints were achieved. We can operate more safely if we are programmed for risk management from the outset. All this can be achieved easily, with the application of machine learning and artificial intelligence. Now is the time to move forward.

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How machine learning and artificial intelligence can drive clinical innovation - PharmaLive

The company said the application of the Mira's AI methodology is part of a strategy to come up with "the best targets, which in turn will produce the best results"

() (OTCMKTS:OQMGF) revealed on Thursday that it had identified more than 25 targets by engaging Mira Geoscience Ltd to conduct geological modelling and exploration on its Alpha property in Val DOr, in Qubec by using Artificial Intelligence (AI).

Toronto-based O3 Mining said it is seeking to actively minimize exploration risks and mitigate costs by using advanced technology.

The company said Mira used drilling and mapping databases, geochemical samples, induced polarization, electromagnetic, magnetic and gravity datasets and other data, to provide a regional scale targeting model of the Alpha Property to help with resource expansion and regional exploration.

READ:O3 Mining intersects 17.8 grams per ton gold at its East Cadillac property in the Abitibi region of Quebec

The Alpha property is an 80 square kilometre property located in the heart of the Val DOr district hosting approximately 40 historical gold and copper-gold zones in numerous geological environments. Therefore, prioritization of drilling targets is key to maximize probabilities to rapidly discover significant mineral deposits on the property, O3 Mining CEO Jose Vizquerra said in a statement.

The application of the Mira innovative AI methodology is part of an integrated strategy to accomplish this task and come up with the best targets, which in turn will produce the best results and bring the most value to our shareholders, he added.

According to the O3 Mining, Mira conducted different targeting exercises combining knowledge-driven and data-driven, or supervised machine learning methods. Once the predictive model was deemed strong enough in making an accurate prediction on known mineralization, the company said it was applied to the voxet to estimate the likelihood of each cell to be mineralized.

By combining the different prospectivity scores produced by the targeting exercises, Mira produced a set of mineral prospectivity indices (MPI) for the different types of mineralization observed on the Alpha Property.

O3 Mining is also executing a stripping campaign on selected mineralized areas to improve understanding of the geology and mineralization controls within the four sectors on the property. The company said the results will be fed back into the Mira modelling to ensure that the AI process is supported with verified field observations. This work which started in Junewill extend into September this year.

The combined Mira modelling, the in-depth knowledge of the property geology and the results of the stripping campaign will provide a strong base to the upcoming aggressive 150,000 metre drilling campaign slated to be executed between September and April 2021, said the company.

Contact the author Uttara Choudhury at [emailprotected]

Follow her on Twitter: @UttaraProactive

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O3 Mining identifies over 25 targets using artificial intelligence on its Alpha property in Qubec - Proactive Investors USA & Canada

Robotics and Artificial Intelligence (AI) are expected to disrupt numerous sectors and industries. But how can investors capitalise on this theme?

Artificial Intelligence, robotics and automation are all themes which are becoming more prevalent within todays society, and for investors, certainly have a lot of potential. We do not yet fully understand and are unable to predict the true impact of these technological advancements, yet the speed at which business and operational transformation is taking place via the implementation of these digital technologies is staggering.

Artificial intelligence (AI) is a branch of computer science which is allowing companies to move to a new standard of analysing data and helping them to garner more value from their assets, both physical and digital. By utilising rapidly growing datasets, businesses are able to drive innovation, increase efficiency and empower this data to generate societal and corporate profits.

Robotics have been around for some time with UNIMATE being the first robot to be used on a production line in 1962. Todays examples include welding robots in factories, order picking robots in goods warehouses and even surgical robots used to improve clinical outcomes of patients through minimally invasive surgery. Additionally, as automation has allowed companies to use software to perform administrative tasks, robots now input digital signatures, auto-filling of online forms and employee analytics. The automation of manufacturing processes has also allowed for greater efficiency and reduced costs.

The intent to embrace these technologies already exists and is growing. In Morgan Stanleys Q3 2019 CIO Survey, artificial intelligence and machine learning implementation was listed as the second highest priority IT spend for companies, preceded only by cloud computing. Traditional business models are certainly being disrupted. The benefits of these new and ever-improving technologies will expand well beyond just technology stocks; they will influence and drive change and disruption through numerous sectors and industries.

The investment case for these themes is clear for anyone to see. However, identifying the correct investments to exploit these substantial opportunities and putting them together in an efficient way is somewhat trickier. Below are a number of actively managed funds which look to capitalise on these increasingly important and impactful themes:

The fund is a particularly unique offering giving investors not only a chance to access companies benefiting from, or set to benefit from, AI but also giving access to an investment process using AI itself. Their proprietary AI platform is used to identify companies where economic value is directly affected by AI.

As more and more companies engage with AI, this fund is well positioned to provide strong exposure to secular investment growth of long duration, resulting in potential for very strong returns. The fund is well diversified and doesnt rely solely on high allocation to the US and tech stocks; however, investors will need to accept a higher level of overall risk.

The team managing and contributing toward the investment process is thought to be the largest dedicated technology investing team in Europe. Their expertise and experience helps them to identify companies standing to benefit and capture the growth created by these long-term transformational themes.

The fund gives great exposure to companies enabling and involved in robotics, automation, AI and materials science. In doing so it has delivered annualised returns of over 15.5% since its inception in late 2017 double that of both the benchmark and sector.

The fund mainly invests in companies contributing to, or profiting from, developments in robotics and enabling technologies. Pictet is arguably the leading thematic investing firm in Europe and their pedigree within this space speaks for itself. On a three-year basis, this fund has generated the highest excess return over its respective benchmark of any of Pictets funds demonstrating the potential of this investment opportunity in particular.

The team believe the robotics sector is set to grow significantly faster than the broader economy over the coming years due to the ability of robotics to increase productivity, reduce costs and help solve challenges such as an increasingly elderly population.

Tom Rosser is investment research analyst at The Share Centre

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BLOG: How to capitalise on the Artificial Intelligence theme - Your Money - Your Money

News Release

Wednesday, August 5, 2020

Collaborative network to enlist medical imaging and clinical data sciences to reveal unique features of COVID-19.

The National Institutes of Health has launched the Medical Imaging and Data Resource Center (MIDRC), an ambitious effort that will harness the power of artificial intelligence and medical imaging to fight COVID-19. The multi-institutional collaboration, led by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), part of NIH, will create new tools that physicians can use for early detection and personalized therapies for COVID-19 patients.

This program is particularly exciting because it will give us new ways to rapidly turn scientific findings into practical imaging tools that benefit COVID-19 patients, said Bruce J. Tromberg, Ph.D., NIBIB Director. It unites leaders in medical imaging and artificial intelligence from academia, professional societies, industry, and government to take on this important challenge.

The features of infected lungs and hearts seen on medical images can help assess disease severity, predict response to treatment, and improve patient outcomes. However, a major challenge is to rapidly and accurately identify these signatures and evaluate this information in combination with many other clinical symptoms and tests. The MIDRC goals are to lead the development and implementation of new diagnostics, including machine learning algorithms, that will allow rapid and accurate assessment of disease status and help physicians optimize patient treatment.

This effort will gather a large repository of COVID-19 chest images, explained Guoying Liu, Ph.D., the NIBIB scientific program lead on this effort, allowing researchers to evaluate both lung and cardiac tissue data, ask critical research questions, and develop predictive COVID-19 imaging signatures that can be delivered to healthcare providers.

Maryellen L. Giger, PhD, the A.N. Pritzker Professor of Radiology, Committee on Medical Physics at the University of Chicago, is leading the effort, which includes co-Investigators Etta Pisano, MD, and Michael Tilkin, MS, from the American College of Radiology (ACR), Curtis Langlotz, MD, PhD, and Adam Flanders, MD, representing the Radiological Society of North America (RSNA), and Paul Kinahan, PhD, from the American Association of Physicists in Medicine (AAPM).

This major initiative responds to the international imaging communitys expressed unmet need for a secure technological network to enable the development and ethical application of artificial intelligence to make the best medical decisions for COVID-19 patients, added Krishna Kandarpa, M.D., Ph.D., director of research sciences and strategic directions at NIBIB. Eventually, the approaches developed could benefit other conditions as well.

The MIDRC will facilitate rapid and flexible collection, analysis, and dissemination of imaging and associated clinical data. Collaboration among the ACR, RSNA, and AAPM is based on each organizations unique and complementary expertise within the medical imaging community, and each organizations dedication to imaging data quality, security, access, and sustainability.

About the National Institute of Biomedical Imaging and Bioengineering (NIBIB):NIBIBs mission is to improve health by leading the development and accelerating the application of biomedical technologies. The Institute is committed to integrating engineering and physical science with biology and medicine to advance our understanding of disease and its prevention, detection, diagnosis, and treatment. NIBIB supports emerging technology research and development within its internal laboratories and through grants, collaborations, and training. More information is available at the NIBIB websitehttps://www.nibib.nih.gov.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

###

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NIH harnesses AI for COVID-19 diagnosis, treatment, and monitoring - National Institutes of Health

Global Artificial Intelligence (AI) in the Freight Transportation Industry Market 2020 report focuses on the major drivers and restraints for the global key players. It also provides analysis of the market share, segmentation, revenue forecasts and geographic regions of the market.

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Artificial Intelligence (AI) in the Freight Transportation Industry Market - Global Industry Growth Analysis, Size, Share, Trends, and Forecast 2020 ...

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence-as-a-service (AIaaS) market and it is poised to grow by USD 15.14 billion during 2020-2024, progressing at a CAGR of over 48% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Please Request Free Sample Report on COVID-19 Impact

Frequently Asked Questions-

The market is concentrated, and the degree of concentration will accelerate during the forecast period. Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

The growing adoption of cloud-based solutions has been instrumental in driving the growth of the market.

Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Segmentation

Artificial Intelligence-as-a-Service (AIaaS) Market is segmented as below:

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR41175

Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. Our artificial intelligence-as-a-service (AIaaS) market report covers the following areas:

This study identifies the increasing adoption of AI in predictive analysis as one of the prime reasons driving the artificial intelligence-as-a-service (AIaaS) market growth during the next few years.

Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Vendor Analysis

We provide a detailed analysis of vendors operating in the artificial intelligence-as-a-service (AIaaS) market, including some of the vendors such as Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. Backed with competitive intelligence and benchmarking, our research reports on the artificial intelligence-as-a-service (AIaaS) market are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.

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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Key Highlights

Table of Contents:

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by End-user

Customer Landscape

Geographic Landscape

Drivers, Challenges, and Trends

Vendor Landscape

Vendor Analysis

Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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COVID-19 Impacts: Artificial Intelligence-as-a-Service (AIaaS) Market Will Accelerate at a CAGR of Over 48% Through 2020-2024|Growing Adoption of...

As humans, we love contrast. It is no wonder that experts, while defining the impact of Artificial Intelligence (AI) on the future of humankind, are at two ends of the spectrum. One is a happy scenario of human beings and artificially intelligent machines coexisting in perfect harmony. Another is an Orwellian dystopia of AI dominance over human intelligence and civilization. While there may be disagreements about the future, everyone agrees on the impact and growing ubiquity of AI.

Let us look at the potential impact of AI on our society. Algorithms have been generally successful in predicting almost all the weather calamities (except, of course, earthquakes) with reasonable accuracies. Since we started using AI, global death share from natural disasters since 2010 has reduced from 0.47% of all world deaths to 0.02% in 2017. AI worked wonders in healthcare by increasing the accuracy and timeliness of disease detection. Using a combination of big data and machine learning algorithms, we can predict machine part failures better. Stability of electricity grids, metal productions and commodity prices are predicted with astonishing precisions.

Enterprises were quick to jump on the AI bandwagon. Big four tech companies are seen to have made most of it. In the midst of the pandemic, global news media on June 9 reported an all-time high share prices for these companieswith a combined market capitalisation of almost $5 trillion. These companies are changing the way we live, do business and relax. We navigate lot more smoothly now with maps on our phone and do not need a translator to understand another language. We have super-efficient digital assistants to manage our schedules intelligently and can buy essential items from our phones.

Consumer packed goods companies have started using big data and machine learning to determine which of the retail stores should get what commodity and at what price. Many of the manufacturing organisations worldwide have started using predictive analytics to analyse their planning efficiency. Using AI techniques, logistics and transportation companies have started planning significant route optimisation, reducing cost and delivering faster to ports. Banks, stock markets and insurance companies use data, machine learning techniques and natural language processing techniques to provide the precise stocks and other financial products recommendations to their customers. Transformative aspects of AI seem to be going beyond delivering powerful use cases and outcomes. It seems to be changing the model of business itself. Organisations are no longer getting measured by the number of employees, assets and real estate they hold. Classic adage of David killing a Goliath is not a fable anymore. AI seems to have the potential to take a powerful business opportunity, analyse a lot of data with powerful algorithms and present the outcome through multiple channels to bring transformation right at the doorsteps (or screens) of consumers. Possibly, that is where it is getting a bit worrisome.

In his 2018 best seller Factfulness, Dr. Hans Rosling points out five global risks that should worry the human race. He could not have been more prophetic. First of them was global pandemic; others being financial collapse, World War III, climate change and extreme poverty. In the middle of a significant disruption, AI is seen to present a real disturbing proposition. Can the enterprise bring in more automation to replace the severely depleted job markets? Can the potential of AI create a situation where powerful corporations and states with the power of algorithm, processing capability of big data get into a position of more unassailable lead - where they have absolute power and society? Would we be left with the intent and resources to focus on most important challenge in post COVID world - more people than ever in the state of extreme hunger?

German philosopher Arthur Schopenhauer wrote, Talent hits a target that no one else can hit, Genius hits the target no one else can see. Human geniuses have their limited time to shape the future as clock ticks on.

(The author is partner, Deloitte India. Views expressed are personal.)

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Experts Are Divided Over Future Of Artificial Intelligence But Agree On Its Growing Impact - Outlook India

LOS ANGELES and MARLBOROUGH, Mass., Aug. 06, 2020 (GLOBE NEWSWIRE) -- RadNet, Inc. (Nasdaq: RDNT), a national leader in providing high-quality, cost-effective, fixed-site outpatient diagnostic imaging services, and Hologic, Inc. (Nasdaq: HOLX), an innovative medical technology company primarily focused on improving womens health, have entered into a definitive collaboration to advance the use of artificial intelligence (A.I.) in breast health.

As the world leader in mammography, Hologic will contribute capabilities and insights behind its market-leading hardware and software, and will benefit from access to data produced by RadNets fleet of high-resolution mammography systems, the largest in the nation, to train and refine current and future products based on A.I. RadNet will share data from its extensive network of imaging centers, as well as provide in-depth knowledge of the patient pathway and workflow needs to help make a positive impact across the breast care continuum. The collaboration will enable new joint market opportunities and further efforts to build clinician confidence and develop and integrate new A.I. technologies.

We believe the future of breast health will rely heavily on the integration of A.I. tools, such as our 3DQuorum imaging technology, as well as next generation CAD software, that aid in the early detection of breast cancer, said PeteValenti, Hologics Division President, Breast and Skeletal Health Solutions. We are energized by the opportunities this transformative collaboration with RadNet creates for patients and clinicians alike. Access to data is critical in training and refining A.I. algorithms. With this collaboration, we now have the opportunity to leverage data from the largest fleet of high-resolution mammography systems to develop new tools across the continuum of care, provide workflow efficiencies, and improve patient satisfaction and outcomes.

As part of its collaboration with Hologic, RadNet intends to upgrade its entire fleet of Hologic mammography systems to feature Hologics 3DQuorum imaging technology, powered by Genius AI. This technology works in tandem with Clarity HD high resolution imaging technology to reduce tomosynthesis image volume for radiologists by 66 percent.i Additionally, all of RadNets Hologic systems are anticipated to feature the Genius 3D Mammography exam, the only mammogram clinically proven and FDA approved as superior for all women, including those with dense breasts, compared with 2D mammography alone. ii,iii,iv,v

The collaboration will be bolstered by RadNets recent acquisition of DeepHealth (Cambridge, MA), which uses machine learning to develop software tools to improve cancer detection and provide clinical decision support. Led by Dr. Gregory Sorensen, DeepHealths team of A.I. experts is focused on enabling industry-leading care by providing products that clinicians and patients can trust. In addition, the DeepHealth team will integrate its A.I. tools within the Hologic ecosystem. When seeking a partner and reviewing options amongst all mammography vendors, we selected to integrate our tools with Hologics market-leading technology, said Dr. Sorensen. Hologics systems produce the highest level of spatial resolution in the market. Hologic also has the largest domestic footprint and market share in 3D Mammography systems. This integration will allow the DeepHealth team to train its algorithms for use with the most advanced screening technology possible. As Hologic and RadNet share their respective capabilities and tools, greater efficiency and accuracy can be achieved by our radiologists.

Much like RadNet, Hologic is a highly innovative company and market leader in breast health, said Howard Berger, MD, RadNets Chairman and CEO. When Hologics leading screening technology is paired with RadNets approximately 1.2 million annual screening mammograms, the resulting dataset becomes a powerful tool to train algorithms. We see the future as being transformative for both of our organizations.

We have witnessed how the application of our Genius AI technology platform has improved cancer detection, operational efficiency and clinical decision support across the breast cancer care continuum, said Samir Parikh, Hologics Global Vice President for Research and Development, Breast and Skeletal Health Solutions. We look forward to building upon these advances in collaboration with Dr. Sorensen and the RadNet team to expand the use of machine learning, big data applications and automated algorithms impacting global breast care.

About RadNet, Inc.RadNet, Inc. is the leading national provider of freestanding, fixed-site diagnostic imaging services in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 335 owned and/or operated outpatient imaging centers. RadNet's core markets include California, Maryland, Delaware, New Jersey and New York. In addition, RadNet provides radiology information technology solutions, teleradiology professional services and other related products and services to customers in the diagnostic imaging industry. Together with affiliated radiologists, and inclusive of full-time and per diem employees and technicians, RadNet has a total of approximately 8,600 employees. For more information, visit http://www.radnet.com.

About Hologic, Inc.Hologic, Inc. isan innovative medical technology company primarily focused on improving womens health and well-being through early detection and treatment.For more information on Hologic, visitwww.hologic.com.

The Genius 3D Mammography exam (also known as the Genius exam) is only available on a Hologic 3D Mammography system. It consists of a 2D and 3D image set, where the 2D image can be either an acquired 2D image or a 2D image generated from the 3D image set. There are more than 6,000 Hologic 3D Mammography systems in use in the United States alone, so women have convenient access to the Genius exam. To learn more, visit http://www.Genius3DNearMe.com.

Hologic, 3D Mammography, 3DQuorum, 3Dimensions, Clarity HD, Genius and Genius AI are trademarks and/or registered trademarks of Hologic, Inc., and/or its subsidiaries in the United States and/or other countries.

Forward-Looking StatementsThis news release may contain forward-looking information that involves risks and uncertainties, including statements about the use of Hologic products. There can be no assurance these products will achieve the benefits described herein or that such benefits will be replicated in any particular manner with respect to an individual patient, as the actual effect of the use of the products can only be determined on a case-by-case basis. In addition, there can be no assurance that these products will be commercially successful or achieve any expected level of sales. Hologic and RadNet expressly disclaim any obligation or undertaking to release publicly any updates or revisions to any such statements presented herein to reflect any change in expectations or any change in events, conditions or circumstances on which any such data or statements are based.

This information is not intended as a product solicitation or promotion where such activities are prohibited. For specific information on what products are available for sale in a particular country, please contact a local Hologic sales representative or write to womenshealth@hologic.com.

Media and Investor Contact RadNet, Inc.:Mark StolperExecutive Vice President & Chief Financial Officer310-445-2800

Media Contact Hologic, Inc.:Jane Mazur508-263-8764 (direct)585-355-5978 (mobile)

Investor Contact Hologic, Inc.:Michael Watts858-410-8588

i Report: CSR-00116

ii Results from Friedewald, SM, et al. "Breast cancer screening using tomosynthesis in combination with digital mammography." JAMA 311.24 (2014): 2499-2507; a multi-site (13), non-randomized, historical control study of 454,000 screening mammograms investigating the initial impact the introduction of the Hologic Selenia Dimensions on screening outcomes. Individual results may vary. The study found an average 41% increase and that 1.2 (95% CI: 0.8-1.6) additional invasive breast cancers per 1000 screening exams were found in women receiving combined 2D FFDM and 3D mammograms acquired with the Hologic 3D Mammography System versus women receiving 2D FFDM mammograms only.

iii Freidewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM, Greenberg JS, Hayes MK, Copit DS, Carlson KL, Cink TM, Carke LD, Greer LN, Miller DP, Conant EF, Breast Cancer Screening Using Tomosynthesis in Combination with Digital Mammography,JAMAJune 25, 2014.

iv Bernardi D, Macaskill P, Pellegrini M, etal. Breast cancer screening with tomosynthesis (3D mammography) with acquired or synthetic 2D mammography compared with 2D mammography alone (STORM-2): a population-based prospective study.Lancet Oncol.2016 Aug;17(8):1105-13.

v FDA submissions P080003, P080003/S001, P080003/S004, P080003/S005

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RadNet and Hologic Announce Collaboration to Advance the Development of Artificial Intelligence Tools in Breast Health - GlobeNewswire

As sports leagues return to the field with lean creative teams and high demand for visual content, PLL is the first organization to debut PhotoShelter AI for visual storytelling, leading to surge in social media engagement.

PhotoShelter today announced its game-changing artificial intelligence solution for creative teams, complete with one-of-a-kind athlete recognition technology. During their 16 day Championship Series, the Premier Lacrosse League (PLL) returns to the field as the first organization to use this cutting-edge technology to move images from the sidelines out to fans in real time.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20200806005300/en/

PhotoShelters collection of AI tools, enabled by Miro AI, takes the PLLs real-time visual content workflow to the next level with end-to-end automation. As soon as a photographer captures a moment and sends the image to PhotoShelter via FTP, the comprehensive AI solution tags photos with metadata specifically designed for the PLL, including player names, sponsor names, and custom terms for core lacrosse gear like goals, gloves, helmets and sticks. The images are searchable and accessible immediately by any PLL staff member for deployment to the PLL's many channels for fan engagement.

RosterIQ athlete recognition technology combines both facial recognition and jersey data to automatically identify athletes. Player tagged images are automatically routed from PhotoShelter to players in real time through the Greenfly app - allowing both the league and its players the opportunity to effortlessly engage fans across social channels.

During the first 2 weeks of the tournament, the PLL team has uploaded 20,000 images to PhotoShelter. So far, PhotoShelter AI has identified 40,666 players and more than 18,000 brand marks. The automated workflow allows the PLL to keep up with the demand for content with a smaller-than-usual media team on site.

"The AI recognition of our photos will help us save hundreds of hours tagging and organizing photos, enabling us to share content with our partners, players, and fans faster than ever before," explained Tyler Steinhardt, Director of Marketing for the Premier Lacrosse League.

The new AI-powered workflow has had a staggering impact on fan engagement. Since the start of Training Camp on July 20, the PLL has performed 2.65M interactions on Instagram - beating out other leagues returning from the sports pause, including the MLS, NWSL, PBR and NASCAR. The AI solution also allows the PLL to distribute content to players faster, leading to a 200% increase in posting by players week over week. On average, players now have a 12.2% engagement rate on Instagram.

"PhotoShelter AI represents a big leap forward in our vision to transform the ways creative people work," said Andrew Fingerman, CEO of PhotoShelter. "PLL is a cutting-edge new sports organization, and they've embraced our technology to drive visual storytelling in real time to new and exciting levels. This is just the first step in our plan to lead a next generation of fully automated content workflow and collaboration capabilities for brands and creative professionals."

In addition to RosterIQ, PhotoShelters suite of AI solutions includes FusionIQ and BrandIQ. FusionIQ pulls together data from three different sources - Google, Microsoft and Amazon - to deliver superior general metadata for tagging a creative team's visual media assets. BrandIQ identifies brand and sponsor logos, to enable creative teams to quickly search and easily deliver relevant assets to each stakeholder in real time.

Although RosterIQ and BrandIQ are ideal for sports, the overall PhotoShelter AI technology suite is designed for the broadest possible use. It has the capability to recognize any set of objects, from staff members and executives to logos and brand marks. FusionIQ will add value through general metadata for any organization, and custom models can be built and trained to meet the unique needs of any PhotoShelter for Brands client.

About PhotoShelter

PhotoShelter is a visual media technology company that helps photographers and creative teams unlock the power of a moment. Our leading digital asset management platform for creative teams helps 1,200 top consumer and retail brands, travel and hospitality icons, professional sports teams and world-class universities easily organize content, collaborate and share their creative assets. To request a demo, please visit libris.photoshelter.com.

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About PLL

The Premier Lacrosse League (PLL) is the leading professional lacrosse league in North America, composed of 7 teams rostered with the best players in the world. Co-founded by lacrosse superstar Paul Rabil and his brother, serial entrepreneur and investor, Mike Rabil, the Premier Lacrosse League is backed by an investment group composed of Joe Tsai Sports, Brett Jefferson Holdings, The Raine Group, Creative Artists Agency (CAA), and other top investors in sports and media. The PLL season is distributed through an exclusive media-rights agreement with NBC Sports Group. For more information, visit http://www.premierlacrosseleague.com

About Miro AI

Miro AI is a growing technology startup based in the United States that uses computer vision and deep-learning to analyze visual media to deliver highly specialized results for sports, events, and businesses. Since launching in 2017, Miro AIs award-winning software has been used by the biggest brands in sports to identify millions of athletes and analyze brand preferences.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200806005300/en/

Contacts

John Seibels Communications Lead PhotoShelter, Inc. jseibels@photoshelter.com

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Game-Changing Artificial Intelligence Solution by PhotoShelter to Revolutionize Social Media Workflow as Premier Lacrosse League Returns to the Field...

By Monojit Mazumdar, Partner and Krishatanu Ghosh, Manager, Deloitte IndiaIn Deloitte Centre for Health Solutions 2020 survey conducted in January 20201, 83% of respondents have mentioned Artificial Intelligence and Machine Learning (AI/ML) as one of their top two priorities.

Conventional wisdom has it that physicians cannot work from home. In the field of healthcare, traditional leverage of AI has been on disease detection and prediction. AI engines have generally been efficient in predicting anomalies in CT scans to detect onset of a disease.

Does it need to remain restricted to detection only? At specific scenario. Many of the Type1 diabetes patients now use a Continuous Glucose Monitor (CGM) to get a near real time reading of their blood sugar levels to determine insulin dosage. These commercially available devices pull the data and load into a cloud based data set-up at a regular interval.

Physicians look at the data during review and suggest adjustment to foods and dosage. A simple AI algorithm can take this further by recommending precise set of treatment recommendations for physicians to validate.

Since routine visits are getting deferred, this simple intervention has the potential to increase both precision and accuracy of the treatment process for all conditions that require timely and routine physician visits.

This opens up the possibility of AI being used as a recommendation tool as opposed to a detection only model. This single change has the ability to transform the entire business model of physical healthcare. From a facility to physically host healthcare professionals along with patients, hospitals and clinics may start operating as a digitally driven operations nerve center.

AI based scheduling service may listen to the patients conditions through a chat bot or voice application. It can ask a series of questions, look at the clinical records of the patients in the system and get a basic hypothesis ready for diagnosis based on data.

It can then schedule an appointment with the most competent physician available depending on the urgency. Before the appointment, the AI engine may prepare a complete briefing with potential diagnosis and recommended treatments. It can answer a set of follow on questions and allow the recommendations to be overridden.

In case of a required diagnostic intervention, AI driven scheduler should be able to arrange for an agent to collect the samples and add them in the patient dossier. Post tele or video consultation, a personal yet non-intervening Voice AI service may do regular follow-throughs, a reminder on medication and other recommended treatment follow through along with any future treatment recommendation. AI engine can sharpen this recommendation by constantly looking through data stream coming from devices that monitor the patient, by consulting physicians.

While this sounds futuristic, we have the technology components commercially available. With a strong and progressively cheaper data network, communication has just got easier. Cloud based storage and delivery of information has cut down the cost of computing infrastructure to a fraction. AI can process faster with advanced hardware gaining speed. Finally, a compulsive situation out of a pandemic has changed our mindset to believe things can be equally good if not better in a remote mode.

Through an efficient sharing of this data with suppliers, typical gaps of demand and supply can be bridged as well. Most important component of making the system work, the need for healthcare professionals may be calibrated as well and with increasing load on healthcare system, a changing model of treatment aided by AI seems to be a good option for future.

DISCLAIMER: The views expressed are solely of the author and ETHealthworld.com does not necessarily subscribe to it. ETHealthworld.com shall not be responsible for any damage caused to any person/organisation directly or indirectly.

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Artificial Intelligence in Healthcare: Beyond disease prediction - ETHealthworld.com