Published: Friday, 21 August 2020. Written by Michiel Fierens, Jan De Bruyne No Comments

Artificial intelligence (AI) is becoming increasingly more important in our daily professional and social lives. One sector in which AI-systems are frequently being used is sports[1], to assist in everything from training to scouting to tactical decision making to doping detection. Considering the many benefits and positive impacts of AI-systems, they will continue to make inroads into the realm of sports[2].

Surprisingly, however, not much (academic) attention has been given to the legal and ethical challenges arising from the use of AI-systems in sports. Against this background, this article examines some important AI applications in sports and then identifies some legal and ethical issues that may need further research. Specifically, it looks at:

Get access to all of the expert analysis and commentary at LawInSport including articles, webinars, conference videos and podcast transcripts. Find out more here.

1215 Posted in American Football | Athlete Welfare | Sports | Cycling | Commercial | Football | Gambling | Articles | Regulation & Governance | Rugby

Michiel Fierens obtained his Master of Laws (cum laude) from the University of Leuven in 2018, with a focus on economic and private law. In 2019, he completed the Advanced Master in Intellectual Property Rights & ICT at the University of Leuven (Campus Brussels) (also cum laude). He started working at CiTiP in September 2019. Michiel is a doctoral researcher mainly involved in the Cybersecurity Initiative of Flanders and the ENSURESEC Horizon 2020-project (Cybersecurity in e-Commerce).

Jan De Bruyne works as senior academic researcher on legal and ethical aspects of AI at the Flemish Knowledge Centre for Data & Society. He is a lecturer and postdoctoral researcher at CiTiP. He also works as a postdoctoral researcher on AI and liability at the Ghent University Faculty of Law and Criminology. He is a member of Leuven.AI. He successfully defended his Ph.D. in September 2018 on a topic dealing with legal aspects of third-party certifiers.

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Artificial intelligence in sports the legal and ethical issues at play - LawInSport

In April, at the height of the lockdown, computer-science professor lex Arenas predicted that a second wave of coronavirus was highly possible this summer in Spain.

At the time, many scientists were still confident that high temperature and humidity would slow the impact and spread of the virus over the summer months, as happens with seasonal flu.

Unfortunately, Arenas' predictions have turned out to be accurate. Madrid, the Basque country, Aragon, Catalonia, and other Spanish regions are currently dealing with a surge in COVID-19 cases, despite the use of masks, hand-washing and social distancing.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)

Admittedly, August is not as bad as March for Spain, but it's still not a situation many foresaw.

Arenas' predictions were based on mathematical modeling and underline the important role technology can play in the timing of decisions about the virus and understanding its spread.

"The virus does as we do," says Arenas. So analyzing epidemiological, environmental and mobility data becomes crucial to taking the right actions to contain the spread of the virus.

To help deal with the pandemic, the Catalan government has created a public-private health observatory. It brings together the efforts of the administration, the Hospital Germans Trias i Pujol and several research centers, such as the Center of Innovation for Data Tech and Artificial Intelligence (CIDAI), the Technology Center Eurecat, the Barcelona Supercomputing Center (BSC), the University Rovira i Virgili and the University of Girona, as well as the Mobile World Capital Barcelona.

The Mobile World Capital Barcelona brings to bear the GSMA AI for Impact initiative, which is guided by a taskforce of 20 mobile operators and an advisory panel from 12 UN agencies and partners.

Beyond the institutions, there is a real desire to join forces to respond to the virus using technology. Dani Marco, director general of innovation and the digital economy in the government of Catalonia, makes it clear that "having comparative data on the flu and SARS-CoV-2, mobility, meteorology and population census does help us react quicker and more efficiently against the pandemic".

Data comes from public databases and also from mobile operators, which provide mobility records. It is all anonymized to avoid privacy concerns.

However, the diversity of the sources of the data is a problem. Miguel Ponce de Len, a postdoctoral researcher at BSC, the center hosting the project's database, says the data coming from the regions is heterogeneous because it is based on various standards.

So one of the main tasks at BSC is cleaning data to make it usable in predicting trends and building dashboards with useful information. The goal is having lots of models running on BSC's supercomputers to answer a range of questions how public mobility is promoting the spread of the virus is just one of them.

Arenas argues that having mobility data is crucial as "it tells you the time you have before the infection spreads from one place to another".

"Air-traffic data could have told us when the pandemic would arrive to Spain from China. But nobody was ready."

Being prepared is now more important than ever. In this regard, the Catalan government's Marco stresses that any epidemiologist will be able to use the tools developed at the observatory. He is convinced that digital tools can help, even though they're not the only solution.

According to Professor Arenas: "We need models on how epidemics evolve, and data is crucial in adjusting these models. But making predictions on the next pandemic is highly difficult, even with AI."

He advocates rapid testing methods, even if some scientists challenge their accuracy, as they could be provide a useful alternative to PCR (polymerase chain reaction) tests, which also have limitations. He also recommends the use of a contact-tracing app like the Spanish Radar COVID, based on the DP3T decentralized protocol.

"A person can trace up to three contacts over the phone. The app enables you to increase that number to six to eight contacts," he says.

SEE:Coronavirus: Business and technology in a pandemic

Oriol Mitj, researcher and consultant physician in infectious diseases at the Hospital Germans Trias i Pujol, agrees that Bluetooth technology can be helpful. But of course, "We should still fight against the idea that it's an app to control the population, because it's not," says Arenas.

Other countries, like Germany, Ireland and Switzerland, have taken the view that if there is any chance of an app making even a small contribution to the battle against the virus, it is worth a go.

Marc Torrent, director of the CIDAI, argues that being able to combine reliable data and epidemiological expertise to improve the management of public resources is already a victory.

The Catalan government has created a public-private health observatory to bring together the efforts and data from a number of bodies fighting COVID.

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AI may not predict the next pandemic, but big data and machine learning can fight this one - ZDNet

Many attempts to develop artificial intelligence are powered by powerful systems of mathematical logic. They tend to produce results that make logical sense to a computer program but the result is not very human.

In our work building therapy chatbots, we have found using a different kind of logic one first formalised by the Greek philosopher Aristotle more than 2,000 years ago can produce results that are more fallible, but also much more like real people.

Read more: The future of chatbots is more than just small-talk

The underpinning science of our chatbots is formal logic. Modern formal logic has its basis in mathematics but that wasnt always the case.

The discovery and formalisation of logic is attributed to Aristotle (384-322 BC) in his collected works, the Organon (or instrument).

Here he documented the first principle of reaching a conclusion from a set of premises. This would be later called inference, guided by rules known as syllogisms.

Since the 20th century, the field of logic has moved away from Aristotles approach towards systems that use predicate and propositional logic. These types of logic have been developed by mathematicians for mathematical applications; hence they are referred to as mathematical logics. Their reasoning is required to be infallible.

Human reasoning, on the other hand, is not always infallible. We mainly reason via deduction, induction and abduction.

You can think of deduction as using generalised rules to reason about a specific example, while induction and abduction involve looking at a collection of examples and trying to work out the rules that explain them.

While deduction tends to be most accurate, induction and abduction are less reliable. These are complex processes not easily programmed into machines.

Arguably, induction and abduction are what separate human intelligence, which is vast and general but often inaccurate, from the narrow yet increasingly accurate intelligence of machines.

We have found that using mathematical logic makes our chatbots less able to have meaningful interactions with humans.

For example, a single human utterance often makes little sense without a large context of what linguists call entailments, presuppositions and implicatures.

While our brains factor in this context automatically, machines must use some form of equivalent logic.

One school of thought suggests parts of Aristotles logic, nowadays referred to as term logic, and his rules of inference, could form core components of an artificial general intelligence (AGI).

The OpenCog and OpenNars are prominent AGI research platforms with term logic at the core. At present these platforms are capable of general-purpose reasoning for potential applications in health and robotics.

Term logic is composed of basic units of meaning, which are linked by what linguists call a copula. To write a bird is an animal in term logic, we could use the copula denoted -> which intuitively means is a special kind of, like this:

Bird -> Animal

This is a very simple example, but more complex and expressive statements are also possible.

Term logic and syllogisms also avoid some of the logical paradoxes that often occur when fitting natural language into a logical framework.

For example, in most systems of formal logic, a nonsense statement like if the moon is made of cheese, the world is coming to an end counts as a valid argument. (This is called the paradox of material implication, and occurs because if often has very different meanings in natural language and in formal logic.)

Aristotle, however, stated syllogisms are what must follow from two independent premises that share one (and only one) term. This rule lets us dismiss the argument above, as the two pieces of the argument (the moon is made of cheese and the world is coming to an end) dont share a term.

AGI researchers have extended Aristotles syllogisms by allowing conclusions that may be true with a degree of uncertainty (fallible reasoning) as well as those that must be true (like those from deductive reasoning). Term logic readily supports these forms of reasoning.

Now that we can derive conclusions that may be true, we need to identify these as beliefs with a corresponding truth value.

How to determine the truth value of a belief is where some AGI researchers differ. The OpenNars project approach is most similar to the human belief system, where it counts the number of independent pieces of evidence for and against a belief to to determine how much confidence to place in it.

So how can Aristotles voice be heard in our chatbot technology?

At the CSIRO Australian e-Health Research Centre we are developing chatbots to help people better manage their health and wellbeing.

Read more: To stop the machines taking over we need to think about fuzzy logic

We have started to use AGI in our chatbot technology for those with communication challenges and who benefit from technology interactions. Our version is mostly inspired from the OpenNars platform but infused with other components we found useful.

Rather than just computing a response from a sequence of words, responses from the chatbot are derived from the relationships between billions of terms. Beliefs with low confidence can be sent back to the user (for example, a person asking a health chatbot about symptoms) as questions.

In the future we think this will allow for more engaging, deeper and natural interactions between humans and machine. The beliefs and personality of the chatbot will become tailored to the user.

Aristotles 2,000-year-old logic has had a profound influence on Western civilisation. A revamp of his ancient works could very well shift us into a new frontier of human-computer interaction.

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Aristotle and the chatbot: how ancient rules of logic could make artificial intelligence more human - The Conversation AU

August19, 20206 min read

Opinions expressed by Entrepreneur contributors are their own.

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Turning an organization into one powered by Artificial Intelligence (AI) requires everyone's participation and contribution. Even though transformation takes time, multiple tactics can begin democratizing AI right away. It has often been said that crises reveal real character, both in individuals and in organizations.

Crises force organizations to rethink how they work and are often the source of lasting change and growth. The Covid-19 pandemic is a humanitarian crisis more enormous than any recently experienced. This situation has raised the importance and prominence of technology. As it recovers from human and economic ravages, AI is positioned to play a critical role.

Entrepreneurs must fundamentally change their culture to one that embraces data, experimentation, and agile principles.

1. Entrepreneurs can easily migrate to the digital environment.

Digital and exponential transformation change the way companies and entrepreneurs run, optimizing processes, and evolve business models by incorporating exponential technologies such as artificial intelligence, IoT, Blockchain, advanced analytics, machine learning, etc. It is an urgent need for all organizations in all sectors.

Migrating to the digital environment is a first step that goes beyond digitizing or automating some tasks. It is about making that digital environment permeate and evolve all actions within organizations; it also seeks to have an impact on the way of working and on obtaining greater efficiencies and productivity in the strategies that are undertaken to grow businesses or improve customer experiences. Likewise, its objective is to determine how regulations are complied with so that a company can improve its risk management or fraud prevention.

In all these processes, analytics allows you to convert data into intelligence to make better business decisions and become relevant. This is how SAS has helped the world's leading business organizations grow and transform by optimizing their operations and increasing their productivity.

Additionally, artificial intelligence technology is no longer reserved for Fortune 500 tech companies as the number of small businesses and entrepreneurs entering the AI market is rapidly increasing. Here are the ways entrepreneurs can leverage artificial intelligence to transform the way they do business

2. AI can help build better marketing strategies for entrepreneurs

Machine learning algorithms know us better than we know ourselves. The ability to analyze data to increase sales is one of the most lucrative applications of artificial intelligence. AI algorithms can sift through large volumes of user data for patterns and trends, which could open the doors to more effective marketing and inform your content strategy. For instance, a recent report on Harvard shows that screening chats for words and phrases correlated to successful sales can improve success rates by 54%.

3. Using Artificial Intelligence to get to know customers

Entrepreneurs and their marketing professionals must be up to date with news in social networks and how Artificial Intelligence changes their working ways. For example, something that cannot be missing in the marketing and sales departments: study the users' search habits and discover the demographics to develop the strategies with your ads.

Also, rather than guessing your target audience and customers' behaviors, AI uses data to conclude the various ways your business can serve them better. AI can also be used for data drive optimizations, improving the conversion rate on your website, analyze your customers' buying patterns, and generally excellent user experience.

4. AI as a competitive tool on social media

In business, knowing what a competitor is doing is as important as making your business plans. AI has helped online marketers track their competitors' activities, learn what they have done on their social media profiles to use that information, and reevaluate their business plan. For example, review the visual content, what is consumed the most, what types of interactions users have with it, and what kind of platform(s) work best.

5. Artificial Intelligence in social networks to improve the user experience

Entrepreneurs can leverage on Artificial Intelligence on social media to get to their target audience. The number of social media users is approaching 2.8 billion, a large enough to immerse themselves in the world of Social Media. As more users join the platforms, the more work is needed, and the more elaborate the strategies will have to be.

6. To build task-centric applications

AI algorithms can substitute human workers for various tasks, freeing up time, money, and resources. Although doomsayers may see it as the beginning of the robotic takeover and the inevitable loss of jobs, most entrepreneurs' trend is a more realistic view.

AI is the perfect match for a human worker. Technology streamlines administrative tasks, allowing humans to focus on more exciting and creative jobs in more essential areas. As an entrepreneur, you can take a cue to outsource a part of your work to AI, thus reducing the number of employees you bring on board, and the amount of work to be done.

Moreover, as an entrepreneur, the onus falls on you to make stringent decisions daily, and which is where AI comes in; it can be set up and harnessed to advise you against making hasty and wrong decisions daily. This can be achieved when AI goes through your work data with an insight to make critical business decisions, whatever it is.

7. Business process efficiency

Improving day-to-day operations can help your business grow faster, and machine learning algorithms are ready to do the heavy lifting. The IA can analyze systems like supply chains and workflows to identify areas for improvement. With optimized workflow, resources are used more efficiently, and costs associated with lost time, maintenance, and redundancy are minimized. The manufacturing sector can benefit considerably from the integration of AI.

Before investing in AI applications, make sure you have all the tools you need to make it work. Calculate the Internet speed you will need, decide which specialists you might have to hire and plan for additional costs you might incur.

As an entrepreneur, keep in mind that you don't need to hire a data scientist to integrate AI into your business. There are tons of companies that design AI tools for entrepreneurs and small businesses. For example, an AI-powered application like Grammarly can help you write consistently and with high quality across your brand, while tools like Legal Robot help you create transparent and compliant legal documents and contracts.

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How to Leverage on Artificial Intelligence to Transform the Way Entrepreneurs Do Business - Entrepreneur

Everyone knows that AI has made a great entry in our lives and now everyone is totally dependent on its services which are not less than a miracle. There is hardly any field left that is not being touched by Artificial intelligence. AI has revolutionized the way of almost all industries. Yes, all industries have started making use of AI these days in different ways.

In this blog, we will discuss how it is playing an important role in reinventing Human resources. Well, it has transformed the way of the HR department by automating tasks like hiring, onboarding, learning, and development, allowing HR teams to focus more on creative and strategic work.

According to a survey, IBM recently released a research report titled The Business Case for AI in HR.

It summarizes IBMs extensive experience in implementing AI into its HR processes and talent management strategies. One key insight from the report is that if HR is working with a trusted technology partner rather than developing its own AI application, you do not need AI expertise but rather an analytical approach, the desire to understand how the technology works and how to use it effectively.

According to the report, what matters just as much as that analytical approach is for HR to be much more knowledgeable about their organization. This is particularly important when it comes to AI applications because they often work across functional areas to serve employees. For example, assignments overseas can now integrate with career development, so that relevant new role opportunities are flagged directly to suitable employees.

Now, its time to discuss the benefits of Artificial intelligence in HR. but before that lets have a look at the compact definition of Artificial intelligence.

Source: Freepik

AI is the latest technology which makes machines to perform the task with intelligence. These machines do not perform like traditional machines which were used to programmed for some specific, repetitive motion. Instead, these machines are capable enough for adapting to different situations.

Well, automation is one of the most important advantages of Artificial intelligence in the field of HR business. With the help of Artificial intelligence, things like repetitive and time-consuming tasks can be done easily and quickly. With the help of AI-based software, you can analyze numbers of job networking sites by scouring through candidates online presence via social media platforms and predict how likely they are to accept the role if offered to them. You can find an amazing tracking system in these AI-based HR tools which run on algorithms and are really helpful in decreasing the burden of HR acquisition and management tasks.

Source: Freepik

We can understand that going with resume screening is not that difficult and it does not require rocket science or other super skills but at the same time it could be said that it is hectic work. There is only a simple requirement that every candidate needs for the resume screening in order to get shortlisted and plenty of time.

So this is the place where AI places humans equally. The only thing that is required is to provide the information to the AI platform and then it will do all things itself. Besides the screening of the resumes, this AI can be moved into the database in order to find those candidates who are looking for new job openings.

Source: Freepik

There is no doubt that the Recruitment process is very hectic and it becomes very difficult to find the right requirement for the job placement. AI can lower the burden of recruitment in the form of chatbots. Yes, chatbots are really helpful in lifting up the burden of the Recruiters by handling the work of social media platforms and company pages so that the necessary information could be assembled properly. So, this is really helpful for the HR staff to have much focus on the interview process instead of hectic technical and repetitive tasks.

You might have seen that customer support chatbots have become so common and are being widely used in all industries. And this can somehow do amazing work in the field of HR for the staff members. This may be really helpful in the HR department in order to get feedback, queries and reports of the whole organization with the help of a centralized support system.

As it is already discussed that AI provides us smart chatbots as a human resource and these chatbots can perform tasks and can respond like human beings to the queries of the various employees which are usually given by HR professionals. So, in this way, HR professionals can spend more time in useful activities like creating a happy and more productive workforce.

After the completion of the hiring process, AI tools can be used to streamline and improve the subsequent onboarding and training. New employees usually require a lot of HRs attention, but much of this attention can be greatly reduced by using AI programs to introduce them to various aspects and take them through routine procedures of the organization.

This is obvious that there are multiple tasks in the organization and it is also very complex to handle and schedule those tasks depending upon the complexity of the particular task. Well, this is usually for those employees who are stuck with personal problems and are not that much particulate in scheduling and maintaining the companys calendar regularly. At this time, AI can play a crucial role in maintaining all these tasks like an individual staff members personal calendar and alert them of upcoming duties, meetings, and deadlines for good measure.

AI is also very helpful in alleviating the retention issues of the company. Staff members with access to a support system that can redirect them to any manager or development opportunity at the touch of a button are far more likely to be content with their job placements. The only thing worse than absent HR ear is a lack of attention towards the staff these can be amended by AI implementation.

So this is how AI is revolutionizing and reinventing Human resources. Well, I have tried to explain this on the basis of certain parameters. Ai is contributing to almost all the fields and similarly like it is making its best contribution to the HR department.

Now you are acquainted very well with the benefits of Artificial intelligence in Human Resources. Now you can hire AI developers in India to develop a good mobile app for you.

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How Artificial Intelligence Is Reinventing Human Resources? - Customer Think

The No. 1 overarching hot topic at all the medical conferences over the past couple years has been artificial intelligence (AI). What was once science fiction or far-fetched research projects are now starting to gain U.S. Food and Drug Administration (FDA) market clearance. Some AI elements are already being used without clinicians knowing it, being integrated into the backend of cardiology imaging systems and IT reporting systems to help speed workflow.

However, beyond the hype of AI, there are practical concerns, including the need for validation, clinical evidence showing AI helps patient care, and the payment system based on how medicine did things 20-30 years ago needs to change.

We have a huge gap between all this AI investment and how we actually take care of patients. We need to integrate it into our care, because if it is not part of how we take care of patients, this isnt going to work, explained John Rumsfeld, M.D., Ph.D., FACC, American College Cardiology (ACC) chief innovation officer, and professor of medicine at the University of Colorado School of Medicine. The clinical evidence needs to be there, and right now there is way more hype for artificial intelligence. We need to build that evidence and we also need alignment with our payment models.

Rumsfeld said there is a need to reverse engineer the current cardiac care system used by most hospitals if AI is going to play a big role. The reimbursement system also needs to change in order for the investment in AI to be justified. For these reasons, the Rumsfeld is involved with an ACC project called the Roadmap for Innovation, which is trying to coordinate cardiology AI efforts by working with AI vendors directly. He said there is a tremendous amount of investment in AI for healthcare, but to date there has been very little of this translating into changes in the way cardiology care is delivered.

Rumsfeld also discussed the current ACC efforts to advance evidence-based implementation of AI in cardiac care, including applications for ACC National Cardiovascular Data Registry (NCDR).

The ACC has partnered with numerous AI companies to co-develop technologies useful for cardiology. He said this includes integrating AI-enabled smartphone apps and patient monitoring devices like Apple Watches directly with electronic medical record (EMR), a remote monitoring platform for heart failure patient management, and use of natural language processing to get more meaningful data out of the EMR than can be found in structured data. This last point is aimed at using AI to reduce the staff burden now involved in data collection for registries and trials.

What I am hoping is that we can get from where we are today to actually lead the digital transformation in healthcare, Rumsfeld explained.

Another area where ACC offers an advantage to AI developers is its access to large amounts of data from its NCDR. For example, ACC is partnering with Yale University to use AI developed there and NCDR data to find where AI can make a difference in terms of outcomes.

The best technology is what they call quiet technology, you dont even know its there and it makes you more efficient and does a task. In the non-healthcare world we dont even realize when AI is being used, Rumsfeld explained. This includes use of AI to automatically create local weather forecasts based on your cell phone location, traffic congestion maps based on real-time AI analysis of big data, instant driving directions to any location with alternate routes and estimate travel times, or even intelligent algorithms that power internet search engines.

As cardiologists we are comfortable with advances in technology, but we need it to be efficient, useful and make our lives better and not worse like many would argue the EMR has done, and it really should be in the background. But if it is going to run in the background, we need to know it has been clinically validated and that we have shown it is safe, effective and actually does what you need it to do, Rumsfeld said.

Hear more from Rumsfeld on this topic in the VIDEO: ACC Efforts to Advance Evidence-based Implementation of AI in Cardiovascular Care.

Under the umbrella of AI is machine learning, which uses complex analytical software to problem solve, explained Anthony Chang, M.D., chief artificial intelligence officer, Childrens Hospital of Orange County (CHOC), and founder of AIMed, an organization that is attempting to facilitate discussion, connections and collaboration between the various companies and hospitals developing AI. He said machine learning will eventually plateau with its abilities to solve more complex problems, which is where the newest iteration of AI, deep learning comes in.

This technology uses convolutional neuro networks to learn from mistakes and experience gained as it performs tasks, operating very similar to the human brain. When it comes to AI in the future helping manage complex patients, this is the technology that can do it.

Deep learning is harder to do and it requires a lot more data, but I think the dividends for particularly complex situations are going to be much bigger than anything we have seen before, Change said.

For example, a complex heart failure patient might have several other chronic comorbities, take numerous drugs from different doctors that might have interactions, and they may have a long list of tests and past procedures. Chang said deep learning systems will be able to sort through all the patients data, prior and current lab test results, radiology reports, ECGs, and procedure reports and very quickly offer suggestions for how to best manage the patient based on current guidelines or recent large clinical study data.

He said a big advantage of deep learning is that you can give the computer a lot of data where you know the outcome and then you let the computer sift through it. The AI then determines how to create a new algorithm needed to improve risk stratification.

This is a paradigm, because we do not set the rules, we let the computers figure it out, and then we take that algorithm developed by the computer and apply it to new patients and see if it makes sense, Chang explained. We should not let the algorithms be the only solution, there should be cognition as well, where there can be collaboration between humans and the machine to get the best outcome.

Many AI apps have been developed to aid identification of various radiological findings in medical images. However, the development of these automated detection algorithms are similar to training a computer to identify a cat or dog using thousands of photos. In cardiology, patient issues usually are much more complex than what is seen alone in the imaging.

Cardiology is one of the best fields to use AI, because it has this set of problems, like complex patients, the need for decision support, wearable technology and the AI needed for that. Cardiology certainly has the portfolio of problems with the solutions that can be engendered by artificial intelligence, Chang said.

AI is already helping augment cardiologists and medical imaging. Examples of AI commercially available today include automated ejection fraction (EF) calculations for point-of-care ultrasound systems (POCUS), like on the GE Healthcare Vscan. Premium cardiac ultrasound systems like the Philips Epiq and the Siemens SC2000 use AI to automatically identify the anatomy, segment, label it, identify the optimal echo views and perform automatic measurements before the physician begins to read the case.

The GE Healthcare Vscan app LVivo EF offersAI-automated ejection fraction calculations. The vendor said this has been helpful in assessing COVID-19 patients at the bedside without the need to use full-sized echocardiography systems that need to be sterilized following an exam.

A couple CT software vendors now offer AI-automated calcium scoring software for cardiac CT scans, creating the report quantification information in seconds and color coding the calcium by vessel segment on the dataset slices. In the past year, GE healthcare and Canon gained FDA approval for AI-based CT iterative reconstruction algorithms to enable high diagnostic quality images from very low-dose CT scans. Siemens uses AI to isocenter patients on the scanning bed to aid in optimizing CT images.

Comparison of a stabndard CT scan interative reconstruction method (left image) and artificial intelligence assisted CT image reconstruction for lower-dose CT scans using the Canon Aquilion Precision CT AiCE software.

Arterys AI-based cardiac MRI analysis software automates the quantification required to speed exam post-processing. AI is also being applied to speedup exam times and enable multiple protocol images to be created from a single scan, greatly reducing the time patients need to spend inside the MRI machine, allowing more patients to be scanned per day.

Another example is an AI app developed at St. Luke's Mid America Heart Institute to automatically risk stratify atrial fibrillation (AFib) patients. The Epic-based app stratifies patients into those who should be placed on anticoagulation, those who do not need anticoagulation, and those who are candidates for left atrial appendage (LAA) occlusion, explained Sanjaya Gupta, M.D., electrophysiologist, St. Luke's Mid America Heart Institute, and assistant professor, University of Missouri Kansas City School of Medicine. He said this has greatly enhanced the patient selection process for LAA occlusion procedures.

Most importantly, this helps us identify those patients that we did not realize had a problem, thats what is really key. This also helps us identify as risk patients between their regular clinic visits and allow us to call and intervene. That is really where the next level of this is going and will make a big impact on patient care quality, Gupta said.

Watch an interview with Gupta in the VIDEO: Artificial Intelligence to Automatically Risk Stratify Atrial Fibrillation Patients.

AI algorithms are being used to automatically detect arrythmias and send alerts to patients using wearables or smartphone-based apps that record ECG. Examples of this technology are the Apple Watch and the Kardia Alivecor device. AI will likely see its biggest steps forward in cardiology for point-of-care (POC) triage apps and wearables cardiac monitoring technologies. This will speed the process of getting at-risk patients examined by a human cardiology specialist and aid in earlier detection of cardiovascular diseases.

Applications for Artificial Intelligence in Cardiovascular Imaging

VIDEO: Artificial Intelligence Applications for Cardiology Interview with Anthony Chang, M.D.

Canon Medical Introduces Deep Learning-Based CT Image Reconstruction

VIDEO: ACC Efforts to Advance Evidence-based Implementation of AI in Cardiovascular Care Interview withJohn Rumsfeld, M.D.

VIDEO: Example of Artificial Intelligence Integrated Into Cardiac Ultrasound

VIDEO: Use of Artificial Intelligence To Speed Cardiac Clinical Research James Januzzi, M.D.,

How Machine Learning Empowers Echo Users Today

VIDEO: How Hospitals Should Prepare for Artificial Intelligence Implementation Interview with Paul Chang, M.D.

How Artificial Intelligence Will Change Medical Imaging

PODCAST: Fitting Artificial Intelligence Into Cardiology Interview with Anthony Chang, M.D.

How Will Artificial Intelligence Impact Healthcare?

VIDEO: Artificial Intelligence for Echocardiography at Mass General Interview with Judy Hung, M.D.

VIDEO: Example of Artificial Intelligence Integrated Into Cardiac Ultrasound.

VIDEO: Overview of Artificial Intelligence and its Use in Cardiology Interview with Anthony Chang, M.D.

Combatting the No. 1 Cause of Death With the Help of Artificial Intelligence and Advanced Technology

VIDEO: Use of Technology to Address Underserved Populations Interview with Partho Sengupta, M.D.

Three High Impact AI Market Trends in Medical Imaging at RSNA 2019

Technology Report: Artificial Intelligence

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Artificial Intelligence Applications in Cardiology | DAIC - Diagnostic and Interventional Cardiology

Lately Ive been reading a lot in newspapers and magazines about Artificial Intelligence. The name refers to computers and such machines that duplicate the thinking process of real intelligence, which is done by the human brain.

Artificial Intelligence is always capitalized, unlike real intelligence. One might expect that to be the other way around, since human beings used the real intelligence they were born with to create the machine with the artificial kind.

It also seems strange that human beings can use their uncapitalized intelligence to create machines that think better and faster than they do.

I did some poking around the vast amount of material about Artificial Intelligence on the Internet, and came upon what seem to be the earliest stirrings of the phrase.

A conference at Dartmouth University was organized in 1956 by four scientists to consider the proposal that"every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it".

Many of the scientists and mathematics experts who attended became important figures later during the early decades of research on the subject.

John McCarthy, who had begun as a child prodigy mathematician and was then on the faculty at Stanford University, was the attendee who suggested Artificial Intelligence as the term to describe computer programs which seemingly exhibit intelligence, by performing tasks which would require a human to be intelligent.

Dont ask me for any details about Artificial Intelligence mathematics, or plain old genuine intelligence mathematics, for that matter. I was always good at handling words and facts, but numbers tend to tumble around in my noggin and often fall out.

So I appreciate what the Artificial Intelligence industry is doing now for guys like me. I read a list in a magazine recently of some of the things computers now do with programs in their installed Artificial Intelligence (and most likely never make a mistake, as most of us humans are prone to do occasionally.)

Translate foreign languages into English.

Provide legal advice.

Cook meals and do other household chores (often with voice commands.)

Study a medical patients test result and produce a diagnosis.

Provide limited vision for some who are vision-impaired.

Evaluate the emotional intelligence of a patient and give advice as a trained psychologist.

Strangest of all in the predictions of what Artificial Intelligence may do is the idea that as these brainy computers put together giant amounts of data and information, it will speed up advancements in science and technology.

One article on the subject maintains that as Artificial Intelligence continues to put together giant amounts of information, it will gradually speed up the advancing of technological change.

Some experts say that Artificial Intelligence may in a single generation produce more technological breakthroughs than the human race has accomplished during the first 20,000 years of its existence!

Sounds impossible. But then I think back to the days when I would stand on a stool and crank up the phonograph so my grandfather could listen to records, and today my little great-grandson watches things on his little computer,and I wonder what new technology he will be taking for granted by the time hes an old retired guy.

I wonder what Artificial Intelligence will be up to then.

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Of All Things: The reality of Artificial Intelligence - Montgomery Newspapers

Instead of relying on only a physical barrier, what if scientists were to develop a substance capable of killing COVID-19s viral particles when they come in contact with personal protective equipment? And in cases where the virus evades these protections, what if programming software could give radiologists an extra set of eyes when diagnosing the disease?

Among the engineering-based, University-researcher-led projects set into motion by the COVID-19 pandemic, two have sought to answer these questions in recent months. These projects, which received a kick-start from the Universitys COVID-19 Research Seed Fund, arose from collaborations between University researchers, a transnational company and state hospitals.

A silver lining

Personal protective equipment once filled the shelves of storage rooms in hospitals and other potentially hazardous workplaces in full stock. But in the wake of COVID-19, boxes of face masks started to empty just as these supplies became an imperative addition to peoples wardrobes.

As masks have become a mainstay of outings and extra PPE has become necessary in situations where social distancing cannot be maintained, industries and researchers have joined forces to improve the protection these materials offer.

Graphene Composites, a company based out of the United Kingdom with a subsidiary at the CIC Providence in Rhode Island, specializes in the creation of nano-materials technology and has partnered with researchers at the University in its creation of graphene ink an antiviral and antibacterial substance for coating PPE. If the substance is deemed effective and approved for use, equipment covered with it would be able to not only trap viral particles but also eradicate them upon contact.

Since GCs inception, the company has sought uses for the worlds strongest material, graphene, and the worlds lightest, the shock-absorbent aerogel, said Sandy Chen 88, the companys CEO and founder. Their prior products have included bulletproof shields for schools, durable wind turbines and fabric coating that creates a scent-based invisibility cloak to protect against mosquitos.

The company began developing the active component of its COVID-19-fighting substance in March and worked on it into early June. They have since passed the product into the hands of researchers at Brown and the United Kingdoms National Physical Laboratory for concurrent examination.

The active portion of this ink that could shield people from the coronavirus consists of many ultrathin sheets of graphene that are singly studded with roughly 10,000 silver nanoparticles, Chen said.

Scientists have known of silvers germ-killing properties for years. But these silver molecules are much smaller than a SARS-CoV-2 invader. To ensnare the virus, flakes of the silver-coated graphene band together, allowing the silver to destroy the virus protective coating, jeopardizing the genetic material within so that the virus can no longer multiply. The graphene should thus stop the virus from passing through the PPE it coats.

GCs collaborators investigating the products efficacy include Assistant Professor of Molecular Microbiology and Immunology Amanda Jamieson, the principal investigator on the University side of the project, Investigator Meredith Crane PhD12 and Postdoctoral Associate Nivea Farias Luz. The decision to involve the University was entirely logical given that theres a real character at Brown that is about community involvement and doing things for the greater good and this (project), if anything, is exactly that, Chen said.

Although lab regulations prohibit University researchers from studying the live SARS-CoV-2 virus, they have utilized the influenza virus to test how well their product can kill a virus, Jamieson said. The researchers hope these experiments will eventually be performed with the coronavirus and other infectious agents. GC hopes to roll out results in the coming months.

Jamieson and her fellow researchers at the University are conducting assay experiments, which allow them to measure how Graphene Composites ink impacts the quantity of viral particles lingering on PPE.

GC is partnering with other manufacturers to obtain the PPE for coating. Weve done a good job building a coalition both locally, regionally and even nationally to distribute the products once the testing has been completed, President of GC USA John J.R. Pagliarini said.

To ensure that the final coating is long-lasting, the researchers must also develop different base solutions the liquid containing the tiny graphene-silver compound for varying PPE materials, such as fabric, plexiglass or other materials being coated. Graphene alone in large quantities can be hazardous, but what weve made sure of is that (the graphene is) not going to come off and cause harm, Chen said. Eventually, the researchers hope their ink could be used on everyday materials, like sports equipment or phone cases.

Coming in right in the middle of everything going on with this project, it was very clear to see that the motivation is not because its a very great financial opportunity but because its a very urgent need, and I think everybody in our team has been motivated to push that need and to find a solution, Charlotta Gustavsson, the summer business intern at GC, said.

These are unprecedented times as we all know, Pagliarini said. We are fortunate because we have the ability within this company to do something profound and transformative.

A picture worth a thousand words

Adequate COVID-19 testing remains a major obstacle in curbing the virus spread. One COVID-19 testing method employs a laboratory technique, the polymerase chain reaction, to identify whether the SARS-CoV-2 virus genetic material, found in the form of RNA, is present in a person. This technique, while unaffordable in some countries and not always accurate, can be coupled with diagnostic and prognostic imaging like CT scanning, which has gained prominence in areas with limited access to other testing techniques.

Since February, University researchers have been working to develop (artificial intelligence)-based diagnostic and prognostic software or tools that can be used to assist physicians (in) diagnosing COVID-19 accurately using imaging, as well as predict which patients are going to transition to severe diseases, said Harrison Bai, assistant professor of diagnostic imaging, clinical educator and co-principal investigator of the project. This work involves adapting existing algorithms and creating new ones to recognize the characteristics of the new disease.

While radiologists are essential for interpreting the captured diagnostic images in a way technology cannot on its own, they are not immune to error subtle yet crucial details can slip through on occasion, Bai said. This is where AI proves advantageous: AI can help spot the difference between COVID-19 and pneumonia, signs of which can appear identical on a lung scan during the initial analysis.

Additionally, this method of detecting the risk that COVID-19 poses to different patients could allow for more prompt and appropriate treatment plans. It could also help hospitals foresee the space and care they should expect to make available for these patients, thereby enabling a quicker recovery, Bai said.

The researchers recently published a paper detailing the diagnostic applications of their work with AI and medical imaging. Future uses could involve an algorithmic and clinical approach to determining the severity of the disease.

Principal Investigator of the project and Professor and Chair of Computer Science Ugur Cetintemel, co-principal investigator and Assistant Professor of Computer Science Ritambhara Singh and Associate Professor of Medicine Bharat Ramratnam 86 MD93 are also at the forefront of this research. The Seed Fund has further supported University students in computer science who have contributed to the work.

The researchers are in the process of monitoring and refining the technology in a simulated clinical setting without patient involvement. The team aims to have a final tool in use at Rhode Island Hospital and other clinical centers, where it could assist with the diagnosis and prognosis of COVID-19 and similar diseases like pneumonia.

The most important thing is (the) clinical applications, Bai said, adding that the final solution should cater towards the needs of the patients and health care providers who would be using and relying on these AI tools.

Excerpt from:
Now you see me, now you don't: Using artificial intelligence to identify COVID-19, developing a PPE coating to stop it in its tracks - The Brown Daily...

NEW YORK, Aug. 18, 2020 /PRNewswire/ --

Overview:This AIoT market report provides analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2020 through 2025. The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.

Read the full report: https://www.reportlinker.com/p05951233/?utm_source=PRN

While it is no secret that AI is rapidly becoming integrated into many aspects of ICT, many do not understand the full extent of how it will transform communications, applications, content, and commerce. For example, the use of AI for decision making in IoT and data analytics will be crucial for efficient and effective smart city solutions in terms of decision making.

The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to "thinking" networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange.

AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.

While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes. Six key areas that the analyst sees within the scope of AIoT solutions are: Data Services Asset Management Immersive Applications Process Improvement Next Gen UI and UX Industrial Automation

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery and support models.

From the perspective of the analyst, we see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, and SaaS managed service offerings. More specifically, we see 2020 as a key year for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy services industries will lead the way.

As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.

The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.

The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $4B by 2025. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. The analyst sees machine learning in edge computing as the key to realizing the full potential of IoT analytics.

Target Audience: AI companies IoT companies Robotics companies Semiconductor vendors Data management vendors Industrial automation companies Governments and R&D organizations

Select Report Findings: The global AIoT market will reach $65.9B by 2025, growing at 39.1% CAGR The global market for IoT data as service solutions will reach $8.2B USD by 2025 The AI enabled edge device market will be the fastest growing segment within the AIoT AIoT automates data processing systems, converting raw IoT data into useful information Today's AIoT solutions are the precursor to next generation AI Decision as a Service (AIDaaS)

Companies in Report: AB Electrolux ABB Ltd. AIBrian Inc. Alibaba Alluvium Amazon Inc. Analog Devices Apple Inc. ARM Limited Arundo Analytics Atmel Corporation Ayla Networks Inc. Baidu Brighterion Inc. Buddy C3 IoT Canvass Analytics Cisco CloudMinds Cumulocity GmBH Cypress Semiconductor Corp Digital Reasoning Systems Inc. DT42 Echelon Corporation Enea AB Express Logic Inc. Facebook Inc. Falkonry Fujitsu Ltd. Gemalto N.V. General Electric General Vision Inc. Google Gopher Protocol Graphcore H2O.ai Haier Group Corporation Helium Systems Hewlett Packard Enterprise Huawei Technologies IBM Corp. Infineon Technologies AG Innodisk Intel Corporation Interactor Juniper Networks Losant IoT Micron Technology Microsoft Corp. Nokia Corporation Nvidia Oracle Corporation Pepper PTC Corporation Qualcomm Robert Bosch GmbH Salesforce Inc. SAS Sharp ShiftPixy Siemens AG SK Telecom SoftBank Robotics SpaceX SparkCognition STMicroelectronics Symantec Corporation Tellmeplus Tencent Tend.ai Terminus Tesla Texas Instruments Thethings.io Tuya Smart Uptake Veros Systems Whirlpool Corporation Wind River Systems Xiaomi Technology

Read the full report: https://www.reportlinker.com/p05951233/?utm_source=PRN

About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

Contact Clare: [emailprotected] US: (339)-368-6001 Intl: +1 339-368-6001

SOURCE Reportlinker

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Artificial Intelligence of Things: AIoT Market by Technology and Solutions 2020 - 2025 - PRNewswire

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