The avenues of artificial intelligence and machine learning research are expanding widely and rapidly. Meta says it plans to tailor its AI explorations by analyzing the structures and networks of the human brain, hoping to map better deep learning algorithms by patterning them on the neural activities of real human cells. Over at Google, meanwhile, one of its top engineers says he's convinced a chatbot he worked with has achieved human-like sentience.

What does it all mean? And what could it mean for AI applications across healthcare?

We spoke recently with Chirag Shah, associate professor in the Information School at the University of Washington. With expertise include interactive information retrieval and recommender systems, Shah read his recent HITN article on Google's LaMDA spoke about recent advances in computational models and research techniques and discussed some of the challenges, opportunities and risks as AI gains ground in healthcare.

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Talking points:

Shah's work at UW, and his specific areas of research

Where healthcare it right now with AI & ML and where it's headed

Where do you expect we'll be five or 10 years from now?

How AI and similar to and different from the human brain

The ethical concerns facing healthcare AI deployments, now and in the future

The opportunities advanced AI & ML could enable

More about this episode:

Sentient AI? Convincing you its human is just part of LaMDAs jobMetaverse and virtual reality are gaining a foothold in healthcareNew York State Office for the Aging deploys AI robots as companions for older adultsStudy: AI deep learning models can predict race from imaging resultsAI-enabled app evaluates MRI data to help analyze dementiaGoogle and DeepMind face legal claim for unauthorised use of NHS medical recordsNuance, Health Management Academy launch artificial intelligence collaborativeSpotting bias in AI requires a holistic approach, says studyWhat does the future hold for AI in healthcare?

Twitter:@MikeMiliardHITNEmail the writer:mike.miliard@himssmedia.comHealthcare IT News is a HIMSS publication.

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HIMSSCast: What does the future of AI in healthcare look like? - Healthcare IT News

If you have had a cup of coffee lately, you have probably been served by a robot. It may not have been a baristabot that took your order or handed you your latte at your local coffee shop, but somewhere along the line from bean to breve, an intelligent machine most likely played a role in producing your coffee.

Employing robots and other intelligent machines in industrial processes is part of a movement that is often referred to as the automation revolution. While it promises to shape the future of many industries, it is not futuristic.

Intelligent machines are already being employed in ways we never thought possible a few years ago. And now is the time to understand the impact they can have and the best route to using them to optimize labor practices.

Presently, robot density per employee, which is a measure used to gauge the degree to which automation is being embraced, stands at 126 robots per 10,000 employees. While that may seem small, it is more than double the number recorded in 2015, a trend that has some concerned.

In early 2020, Massachusetts Institute of Technology issued a report titled Work of the Future that was developed in part to address a growing anxiety related to the automation revolution.

In its coverage of the report, MIT Technology Review explained the anxiety in this way: Theres a growing fear among many American workers that theyre about to be replaced by technology, whether thats a robot, a more efficient computing system, or a self-driving truck.

While a robot revolution resulting in a large-scale displacement of human workers is a popular concern that has been explored in an endless number of science fiction movies, it misses the broader potential of an automation revolution. Robots benefit industrial processes most by enhancing the efforts of human workers, not by replacing them.

A recent report by The Wharton School at University of Pennsylvania shows that organizations that increase their automation through the use of robots typically hire more workers. This results from robots enhancing productivity, which grows business and demands an increase in non-robotic jobs. Wharton found that jobs were cut more often in companies that have not embraced the automation revolution. By resisting automation, they fell behind competitors, lost business, and had to let employees go.

This new paradigm of robots playing a more integral role in the workplace will not develop in a vacuum. Politically and culturally, people will need to accept intelligent machines and adapt accordingly. The automation revolution will require a shift not only in the way we work, but also in the way we think about work.

In the 1980s, computers entered the workplace. Some resisted, seeing the new technology as a tool that would be used to supplant the systems that were in place at that time.

Today, very few workplaces could survive without computers. Rather than supplanting systems, computers became a tool to optimize systems. Rather than displacing workers, they created a new universe of jobs.

Robots and other intelligent machines offer the same potential to those who are willing to see them as a tool that can be wielded to increase efficiency and productivity. Those who resist will watch from the sidelines as the automation revolution advances.

Scott Heric, Co-Founder ofUnionly, has years of experience helping organizations to raise funds online. He helped develop sales and account management for Avvo, growing from 30 to 500 people over seven years. Heric then took a chief of staff role at Snap Mobile Inc., where he oversaw development of the product, marketing, sales, and account management, leading to the company becoming a leading digital fundraising platform in higher education. His company Unionly was acquired in January of 2020. This piece is exclusive to Broadband Breakfast.

Broadband Breakfast accepts commentary from informed observers of the broadband scene. Please send pieces tocommentary@breakfast.media. The views reflected in Expert Opinion pieces do not necessarily reflect the views of Broadband Breakfast and Breakfast Media LLC.

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Should the Federal Government Regulate Artificial Intelligence? - BroadbandBreakfast.com

The internet gaming industry leverages AI technology to power many things, such as algorithms that guide users to games they may prefer. They collect data based on your actions to forecast exactly what youre interested in to make things easier and more convenient for you, for example. But it doesnt end there, and this post will cover other ways artificial intelligence impacts iGaming.

It offers a more personalised experience

Whether youre playing online games for real money or not, most people desire and expect a tailor-made experience when engaging in the activity. As mentioned previously, AI-based algorithms can collect data to gain insight into player habits and preferences. With these specifics, they can generate helpful projections that will allow the operator or deliver a more personalised experience to the users.

For instance, if you regularly play at online casinos, some may recommend specific titles to you as soon as you log in to the website. The algorithm brings these suggestions, which likely depend on the AI to function.

It can improve online safety

As more and more players engage in online gambling activities through their computers or mobile devices, security measures have become necessary to ensure the gaming environments safety. Artificial intelligence has been shown to help safeguard players privacy and protect their data while processing payment transactions.

A perfect example of this is the SSL encryption. It's essentially a type of digital mechanism that helps in protecting sensitive information during transactions. It ensures that data stays out of the hands of third parties and prevents fraudulent activities like hacking. The most significant concern of players and bettors when playing online is the security of their account details. Hence, most online casinos use artificial intelligence to prevent data breaches or the unauthorised exposure of sensitive information.

It helps against cheating

Thanks to artificial intelligence, online gaming websites are able to detect fraudsters and cheaters effectively. When utilised, the members behavioural patterns are collected, which may determine whether a player is cheating. As a result, players will always be on equal ground, ensuring that everyone is playing by the rules of their desired games and are unable to manipulate the outcome using questionable tactics or additional software.

AI has and continues to pave the way for a better experience in online gambling. With individualised options and recommendations, enhanced security measures, prevention of cheating, and even simulating how real players would react in a game, artificial intelligence has undoubtedly made iGaming more engaging than it was in the past. As the technology behind it continues to evolve, the games will only get more immersive.

See the article here:
The impact of artificial intelligence on iGaming - Business Insider Africa

Artificial intelligence has been making major headway in many areas of the workforce, bringing considerable changes to fields such as architecture, agriculture, sports analytics, etc., and even the law industry. Improving so many fields of work, how has artificial intelligence changed the law industry for the better?

Artificial intelligence has improved the law industry in several ways, such as data processing and legal research, generating the content, and decreasing overall stress. In addition, artificial intelligence has made the law industry more productive, giving lawyers more time to focus on their clients and cases rather than tedious paperwork and information.

The rest of this article will describe how artificial intelligence has improved the law industry.

Artificial intelligence (AI) has improved data processing and research in law practices. It can help with the discovery process, which is one of the most time-consuming aspects of the practice and can present many challenges for attorneys. AI can help facilitate this process by sorting through data and information to locate relevant documents. AI can also sort through various ways the topic may be referenced.

Artificial intelligence can improve data processing as it is programmed to work with patterns and large amounts of data. AI can:

When it comes to legal research, AI cant replace human actions. However, it can improve them by simplifying the processs more time-consuming, data-related aspects. It gives law teams better research capabilities, using algorithms to sort a significant amount of information.

Artificial intelligence can help with case building and content production, analyzing draft arguments, and putting together legal contracts. Although the ability to generate content is limited, it can still help with the initial stages of writing legal contracts and other documents. When it comes to analyzing draft arguments, AI can assist in identifying weak aspects of the argument and locating inaccurate information.

Computers that have been programmed with legal-based information can also assist in the reviewing of contracts and other documents. AIs ability to process large amounts of information can help attorneys find errors and problems in contracts and other legal documents that may have otherwise been missed. By doing this, artificial intelligence can speed up the process of reviewing documents and make it more consistent and accurate.

In addition to the more technical aspects of artificial intelligence, it can assist with case building by improving interdepartmental communication. For example, AI can help legal teams avoid long, arduous meetings while keeping them moving forward on the same page.

Though AI is taking up the more time-consuming tasks, artificial intelligence wont replace lawyers. However, it will instead give them time to focus on other tasks, allowing them to focus on case building and personal relationships with clients.

The legal profession is known for taking up much time, and lawyers have high rates of stress and depression, which can negatively affect their work. Artificial intelligence could significantly improve morale by reducing the long hours and stress that come with the field.

Some people fear artificial intelligence is taking over careers, displacing workers for more high-speed, consistent technology. However, it is quite the opposite in many fields, such as the law industry.

Artificial intelligence holds significant benefits for legal teams and lawyers, decreasing the time spent on data processing and legal research and improving morale and time management. In addition, by not focusing on tedious tasks, lawyers can devote more energy to case building and the more personal aspects of the legal process.

As the field of technology continues to change, seeing new improvements such as artificial intelligence, many other professions will begin to see significant benefits. The law industry is no exception, and artificial intelligence holds a great deal for the future of the legal field.

The senior editor of Legal Scoops, Jacob Maslow, has founded several online newspapers including Daily Forex Report and Conservative Free Press

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How Artificial Intelligence Is Changing The Law Industry for The Better - Legal Scoops

Artificial intelligence (AI) could lead to UK health services that disadvantage women and ethnic minorities, scientists are warning.

They are calling for biases in the systems to be rooted out before their use becomes commonplace in the NHS.

They fear that without that preparation AI could dramatically deepen existing health inequalities in our society.

i can reveal that a new government-backed study has found that artificial intelligence models built to identify people at high risk of liver disease from blood tests are twice as likely to miss disease in women as in men.

The researchers examined the state of the art approach to AI used by hospitals worldwide and found it had a 70 per cent success rate in predicting liver disease from blood tests.

But they uncovered a wide gender gap underneath with 44 per cent of cases in women missed, compared with 23 per cent of cases among men.

This is the first time bias has been identified in AI blood tests.

AI algorithms are increasingly used in hospitals to assist doctors diagnosing patients. Our study shows that, unless they are investigated for bias, they may only help a subset of patients, leaving other groups with worse care, said Isabel Straw, of University College London, who led the study, published in the journal BMJ Health & Care Informatics.

We need to be really careful that medical AI doesnt worsen existing inequalities.

When we hear of an algorithm that is more than 90 per cent accurate at identifying disease, we need to ask: accurate for who? High accuracy overall may hide poor performance for some groups.

Other experts, not involved in the study, say it helps shine a light on the threat posed to health equality as AI use, already quite common in the US, starts to take off in the UK.

Brieuc Lehmann, a UCL health data science specialist and co-founder of expert panel on Data for Health Equity, says the use of AI in healthcare in the UK is very much in its infancy but is likely to grow rapidly in the next five to 10 years.

Its absolutely crucial that people get a handle on AI bias in the next few years. With the ongoing squeeze on NHS budgets, there will be growing pressure to use AI to reduce costs, he said.

If we dont get a hold on biases, there will be a temptation to deploy AI tools before weve adequately assessed their impact, which carries with in the risk of worsening health inequalities.

Lauren Klein, co-author of the book Data Feminism and an academic at Emory University in Atlanta in the US, said the liver disease study showed how important it was it get AI systems right.

Examples like this demonstrate how a failure to consider the full range of potential sources of bias can have life or death consequences, she said.

AI systems are predictive systems. They make predictions about whats most likely to happen in the future on the basis of whats most often happened in the past. Because we live in a biased world, those biases are reflected in the data that records past events.

And when that biased data is used to predict future outcomes, it predicts outcomes with those same biases.

She gave the example of a major tech firm that developed a CV screening system as part of its recruitment process.

But because the examples of good CVs came from existing employees, who were predominantly men, the system developed a preference for the CVs of male applicants, disadvantaging women and perpetuating the gender imbalance.

AI systems, like everything else in the world, are made by humans. When we fail to recognise that fact, we leave ourselves open to the false belief that these systems are somehow more neutral or objective than we are, Dr Klein added.

It is not the AI in itself which is biased as it only learns from the data it is given, experts stress but rather the information it is given to work with.

David Leslie, director of ethics and responsible innovation research at the Alan Turing Institute, is concerned that AI may make things worse for minority groups.

In an article for the British Medical Journal last year, he warned that: The use of AI threatens to exacerbate the disparate effect of Covid-19 on marginalised, under-represented, and vulnerable groups, particularly Black, Asian, and other minoritised ethnic people, older populations, and those of lower socioeconomic status.

AI systems can introduce or reflect bias and discrimination in three ways: in patterns of health discrimination that become entrenched in datasets, in data representativeness [with small sample sizes in many groups often very small], and in human choices made during the design, development, and deployment of these systems, he said.

Honghan Wu, associate professor in health informatics at University College London, who also worked on the study about blood test inequalities, agrees that AI models can not only replicate existing biases but also make them worse.

Current AI research and developments would certainly bake in existing biases from the data they learnt from and, even worse, potentially induce more biases from the way they were designed, he said.

These biases could potentially accumulate within the system, which lead to more biased data that is later used for training new AI models. This is a scary circle.

He has just completed a study looking at four AI models based on more than 70,000 ICU admissions to hospitals in Switzerland and the US, due to be presented at the European Conference on Artificial Intelligence in Austria next month.

This found that women and non-white people with kidney problems had to be considerably more ill than men and white people to be admitted to an ICU ward or recommended for an operation, respectively.

And it found the AI models exacerbated data embedded inequalities significantly in three out of eight assessments, one of which was more than nine times worse.

AI models learn their predictions from the data, Dr Wu said. We say a model exacerbates inequality when inequalities induced by it were higher than those embedded in the data where it learned from.

But some experts say there are also reasons for optimism, because AI can also be used to actively combat bias within a health system.

Ziad Obermeyer, of the University of California at Berkeley, who worked on a landmark study that helped to explain how AI could introduce racial bias (see box below), said he had also shown in separate research that an algorithm can find causes of pain in Black patients that human radiologists miss.

Theres increasing attention from both regulators who oversee algorithms and just as importantly from the teams building algorithms, he told i.

So I am optimistic that we are at least moving in the right direction.

Dr Wu, at UCL, is working on ways to solve AI bias but cautions this area of research is still in its infancy.

AI could lead to a poorer performing NHS for women and ethnic minorities, he warns.

But the good news is, AI models havent been used widely in the NHS for clinical decision-making, meaning we still have the opportunity to make them right before the poorer performing NHS happens.

Using the wrong proxy, or variable, to predict risk is probably the most common way in which AI models can magnify inequalities, experts say.

This is demonstrated in a landmark study, published in the journal Science, which found that a category of algorithms that influences health care decisions for over a hundred million Americans shows significant racial bias.

In this case, the algorithms used by the US healthcare system for determining who gets into care management programmes were based on how much the patients had cost the healthcare system in the past and using that to determine how at-risk they were from their current illness.

But because Black people typically use healthcare less in America, in part because they are more likely to distrust doctors, the algorithm design meant they had to be considerably more ill than a white person to be eligible for the same level of care.

However, by tweaking the US healthcare algorithm to use other variables or proxies to predict patient risk the researchers were able to correct much of the bias that was initially built into the AI model, reducing it by 84 per cent.

And by correcting for the health disparities between Black and white people, the researchers found that the percentage of Black people in the automatic enrollee group jumped from 18 per cent to 47 per cent.

The NHS is aware of the problem and is taking a number of steps. These include:

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Why artificial intelligence in the NHS could fail women and ethnic minorities - iNews

Artificial Intelligence

The manufacturing floors and warehouses have a number of complex issues. Safety is always the first concern, but efficiency follows. Creating workflows is a longstanding technique for improving both factors, but how to improve the results? Artificial intelligence (AI) has begun, in recent years, to creep into processes in manufacturing as it has in all other areas of business. Advanced in AI, networking, and edge devices are bringing another modern technology into the mix Augmented Reality (AR). The combination of AI & AR are the latest attempt to increase safety and productivity.

Shop floors are busy and dangerous places. Since the start of the industrial revolution, there has always been tension between the needs for efficiency and safety. In the early days, safety wasnt that important. However, in the last century, that has come to the forefront and most companies lead with that in their messaging, even if there are some that only do it there. However, productivity still matters. Advanced technologies of the last decades have often been focused on how to improve both factors at the same time.

When it comes to AI, early applications were on two, very separate, extremes. First, early vision AI would look for simple safety problems, such as missing hardhats. Second, AI was used to figure out optimal plant processes and process flows. Due to recent advances, systems can begin to integrate those features and others. One way thats helping do that is AR.

The popular culture seems conversant with virtual reality (VR), as it has been used as a trope in many movies and games have begun moving towards both imitating VR and working in it. While AR had a pop culture moment years ago, with glasses put forward by a large company, that was a failure, and many havent thought much about it since. Simply put, AR is the concept of augmenting reality with technology additions. One simple example is the heads-up display, used in fighter planes for years and in some cars, in a more limited fashion, in more recent years. A sector that has been adopting VR is in surgery, with VR, AI and robots extending the abilities of surgeons to know more as they operate.

A couple of years ago, I covered a company where an inspector would wear a headset through a construction site. The images were stored and then compared later to blueprints. Part of the fun of this column is seeing the changes over time. Technology has advanced, both in AI and networking. What those advanced are beginning to accomplish is a more immediate link between the backend AI systems and the person in the field.

One of the first ways AR is helping in manufacturing is simple. Take a warehouse. When theres a lot of products moving through, checking inventory can be slow. With an AR system, a person can look at a group of boxes, the backend system can count the boxes. Thats just simple visual AI. However, that count can be integrated with inventory and shipping systems, the count can be compared to the expected count, and a display can pop up, for instance, telling the person to look for two more boxes that should also have been in the order.

Another example is in physical safety. Its one thing to notice if someone is wearing a hardhat. Thats now in the must have, more basic visual identification tools. Lets extend that. The visual system can capture warning signs requiring hardhats, gloves, and other safety measures, or even use the GPS information and database content about the manufacturing floor to know what safety measures are required at each place on the floor. Gloves are a good example of how the power of AI can be used to improve safety on the manufacturing floor, said Dr. Hendrik Witt, Chief Product

AiStudio analyzing gloves

Officer, TeamViewer. AI can now not only detect whether you are wearing gloves, but whether you are wearing the right type of gloves for the specific situation, and then immediately notify the worker for a safety correction.

A final example is another safety issue. Background process analytics, using either AI or standard analytics, can be run to estimate potential for fatigue. A person lifting 10 lb. boxes doesnt get as tired as fast as the same person lifting 40 lb. boxes. Reminding people when to take breaks is just as important to safety as making sure the people are working safely.

An aspect of AI/VR that has also slowed adoption is the need for companies to hire experts in that function, experts who arent in vast supply and who cost more. Companies such as TeamViewer are working to simplify the training of the systems, making them as low-code as possible. This is what other cycles of technology have had to do in order to widen adoption. Its also something that is one of my key soapboxes. Very few companies need to have the magical data scientist. What needs to happen is the building of systems that can talk to the line users in the language they know.

Modern augmented reality is about more than the important goal of improving safety, said Witt. Its about understanding process flows, about integrating AR systems with more of the full range of ERP and other backend software, not only with AI. Its about having systems that non-specialists can use to accomplish their own tasks.

The aforementioned glasses were a fad, something cool for a small audience. AR is now being applied to more focused arenas, including in the manufacturing sector. That focus may finally bring the ROI to an investment in AR and AI that will spread both more widely.

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Manufacturing, Artificial Intelligence, And Augmented Reality: Integration Advances - Forbes

New Delhi, First Published Jul 9, 2022, 11:08 AM IST

New Delhi: Aiming to make India a self-reliant country in the field of technologies and innovation, the defence ministry will launch 75 Artificial Intelligence products on July 11 at the first ever Artificial Intelligence in Defence (AIDef) symposium and exhibition to be launched by its minister Rajnath Singh.

Talking to media persons here on Friday, Defence Secretary Ajay Kumar said, We are going to have a programme where a total of 75 products powered by Artificial Intelligence will be launched on Monday. It would be possibly the largest event. The launch of 75 products coincided with the 75 years of Aazadi Ka Amrit Mahotsav.

Highlighting the AI in the defence forces, Kumar said that the nature of modern warfare has changed, and artificial intelligence has played a significant role in all forms.

Also read:Indian Army wants to buy 29,762 night sights for assault rifles

The products to be launched on Monday are developed by the Services Army, Navy and Air Force, DRDO, DPSUs, Startups and the private sector. Some of the 75 products would be for dual-use purposes, including civil use.

The products are in the domains of automation, unmanned, robotics systems, cyber security, human behaviour analysis, intelligent monitoring system, logistics and supply chain management, speech/voice analysis and Command, Control, Communication, Computer & Intelligence, Surveillance & Reconnaissance (C4ISR) systems and Operational Data Analytics.

Besides, a total of 100 AI-enabled products are at various stages of development.

On July 11, the minister will felicitate two top defence exporters from the public and private sectors.

Also read:Fighter jets for INS Vikrant to be bought the Rafale way

In reply to a question, Additional Secretary Sanjay Jaju informed that the defence exports had crossed the highest ever figure of Rs 13,000 crore in 2021-22, with 70 per cent contribution coming from the private the remaining 30 per cent from the public sector.

Ajay Kumar further stated that an AI task force on defence was established in 2018 to provide a road map for promoting AI in defence.

Acting on its recommendations, a Defence Artificial Intelligence Council was set up headed by the defence minister. The council is spearheading the effort.

Last Updated Jul 9, 2022, 11:08 AM IST

Link:
India goes big on Artificial Intelligence in defence; 75 products to be launched on July 11 - Asianet Newsable

The World Health Organization (WHO) and the International Digital Health and AI Research Collaborative (I-DAIR) have signed a Memorandum of Understanding (MoU) outlining their joint efforts to advance the use of digital technologies for personal and public health globally.

Through this agreement, WHO and I-DAIR will work together to harness the digital revolution towards urgent health challenges, while emphasizing equity and greater participation from Low and Middle-Income Countries (LMIC) in the research and development and governance of the digital health and AI space, with particular focus on the inclusion of young researchers and entrepreneurs.

The partnership will focus on achieving these common goals through a multi-faceted approach focusing on promoting scientific cross-domain/cross-border collaboration and implementing innovative digital health long-term solutions, consistent with WHO recommendations and interoperability standards.

The joint activities include inter alia the promotion and the development of new norms and guidelines for the governance of health data as a public good, the building of evidence cases for thoughtful investments in digital health globally, and the strengthening of stakeholders capacities - for instance via the common elaboration of the WHO digital health competency framework.

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I-DAIR is a multi-stakeholder platform for enabling global research collaborations on digital health and for convening stakeholders to develop global public goods aimed at solving issues around the inclusive, equitable, and responsible deployment of data and AI for health. For more information about our projects, we invite you to visit our website.

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WHO and I-DAIR to partner for inclusive, impactful, and responsible international research in artificial intelligence (AI) and digital health - World...

The partnership will bring AI and robotics to AASTMT new campus in Alamin new city and will allow AASTMT students to apply their learning in the company in the USA.

SAN FRANCISCO and ALEXANDRIA, Egypt, July 8, 2022 /PRNewswire/ -- Today, the #1 ranked university in Egypt in quality of learning The Arab Academy for Science, Technology & Maritime Transport (AASTMT) and RobotLAB, the leading educational robotics company, jointly announced signing a first-of-its-kind partnership between the parties to build an artificial intelligence and robotics lab on the campus.

The AI LAB is the first and largest of its kind in MENA region includes:

For Professor Ismail Abdel Ghafar, president of the AASTMT academy, this partnership will bring an enriching experience for the students and the campus and help prepare the students to their future careers.

"AASTMT has been dedicated to fostering a highly professional environment of advancing knowledge, developing human potential, and breaking new intellectual and academic ground with the aim of improving the lives of human beings and of communities all over the Arab and the whole World," said Prof. Ghafar, "Our partnership with RobotLAB signifies a big step into the future of robotics and AI, and we are very excited to introduce this game-changer lab exclusively for our students and the institution and can't wait to see the positive impact it will have on our community," Prof. Ghafar added.

The AI LAB by RobotLABis a turnkey, state-of-the-art modular learning space designed to enable students' rotation between AI stations. Each configuration includes robots and teaching resources and provide learners with practical hands-on activities exposing them to multiple disciplines and various scenarios in which Artificial Intelligence takes control of our lives. It gives students a unique and rich learning experience to ensure that they are ready for their career life in-the-2030's.

"We are excited to partner with AASTMT," said Elad Inbar, RobotLAB CEO, "This partnership is above and beyond a typical deployment of an AI lab, this is beyond providing robots and training materials. This is about supporting the leading university in Egypt by bringing Silicon Valley know-how to the Middle East, and about knowledge-transfer, bringing students from Egypt to the Silicon Valley. Hands-on experience is the best way to learn, and to prepare for the real life post the studential life"

As part of the partnership RobotLAB will host AASTMT students at its offices located in the USA in California and Texas, for hands-on internship with the leading robotics company. Students will learn what it takes to deploy robots in a commercial environment, learn how to program and configure robots to navigate autonomously, relationship with robot manufacturers, participate in sales and marketing meetings, and over to how to repair robots. The students that will be selected for the program will gain massive, real-world, knowledge!

TheArab Academy for Science, Technology & Maritime Transport(AASTMT) is a regional university operated by theArab Leaguewhich runs programs inmarine transportation,business, andengineering.AASTMT started as a notion in the Arab League Transport Committee's meetings on 11th of March 1970. The Academy's inception was in 1972[3]in the city of Alexandria, Egypt. After that it expanded into Cairo.The vision, mission, and core values of the AASTMT reflect its philosophy since its establishment, as well as an affirmation of its desire to be the beacon of science in Egypt and the Arab region. They also highlight that the AASTMT is an effective agent in achieving sustainable economic and social development.

Founded more than a decade ago, RobotLAB is the premier educational-robotics company. The company's innovative use of robots in the classroom was recognized by prominent organizations and won the company multiple awards such as the Best EdTech Company (SxSWEdu), the Gold in education category (Edison Awards), a Game Changer award (RoboBusiness), Best STEM tool (EdTech Digest), and many more. Trusted by educators in more than 2,500 schools, RobotLAB is the leader in the educational-robotics market, ensuring schools' investment in technology won't be wasted. Its flagship product, Engage! K12 is designed to engage students and help them master the skills they need in order to ensure career and college readiness while developing 21st-century skills.

Media Contact:Maria Galvis+1(415)702-3033[emailprotected]

SOURCE RobotLAB

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The Arab Academy for Science, Technology & Maritime Transport in Egypt, to Launch the First Artificial Intelligence Lab in the MENA, Together with...

Redding, California, July 07, 2022 (GLOBE NEWSWIRE) -- According to a new market research report, Artificial Intelligence in Medical Diagnostics Market By Component (Software, Services), Specialty (Radiology, Cardiology, Neurology, Obstetrics/Gynecology, Ophthalmology), Modality (MRI, CT, X-ray, Ultrasound), End User (Hospital, Diagnostic Center) - Global Forecast to 2029,' published by Meticulous Research, the AI in medical diagnostics market is expected to grow at a CAGR of 36.2% during the forecast period to reach $9.38 billion by 2029.

DownloadFree Sample Report Now @https://www.meticulousresearch.com/download-sample-report/cp_id=5312

AI in medical diagnostics consists of AI software and services that aid healthcare professionals in identifying the diagnosis of different diseases. AI-based software solutions can analyze the data from a diagnostic procedure and either help triage patients by flagging abnormal medical images or suggest the healthcare professional a suitable diagnosis. AI in medical diagnostics integrates deep learning, data insights, and algorithms to detect life-threatening and critical diseases. It automates the diagnosis process and reduces the workload on healthcare professionals.

The main factors driving the AI in medical diagnostics market are the growing need for the adoption of AI in medical diagnosis due to the high rate of errors in medical diagnosis, shortage of healthcare professionals, and increasing prevalence of chronic diseases. Furthermore, the high growth potential in emerging economies and the growing number of cross-industry partnerships & collaborations are expected to provide significant growth opportunities for this market.

However, the reluctance to adopt AI technologies due to a lack of trust is expected to restrain the growth of this market to a notable extent. In addition, factors such as regulatory barriers and privacy and security concerns regarding patient data are the major challenges to the growth of this market.

The Impact of COVID-19 on the Artificial Intelligence in Medical Diagnostics Market

The outbreak of the COVID-19 pandemic in 2020 was a global public health challenge. The number of cases was skyrocketing, and many countries had a huge burden on the health system. The COVID-19 disease mainly affects the lungs of the patients. Hence, cardiothoracic imaging in COVID-19 cases is a common diagnostic practice to identify the severity of the disease. The number of research studies using AI techniques to diagnose COVID-19 rapidly increased in 2020. Many studies were focused on describing the diagnosis of COVID-19 from chest CT images using AI technology. Several studies proved that AI models might be as accurate as experienced radiologists in diagnosing COVID-19.

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CT scans were identified as the key modality for diagnosing COVID-19 at the onset of the disease. Healthcare professionals identified the severity of the disease from features like shadows over the patients lungs. A single patient had approximately 300 CT images, which took a doctor a lot of time to analyze with the naked eye. Also, radiologists needed to compare with earlier scans, increasing pressure on the healthcare staff. In such situations, AI-based systems can analyze CT images within 20 seconds, with an accuracy rate above 90% (Source: Nature Biomedical Engineering Journal). In addition, UC San Diego Health (U.S.) engineered a new method to expedite the diagnosis of pneumonia, a condition associated with severe COVID-19. This early detection helps doctors quickly triage patients to appropriate levels of care even before the COVID-19 diagnosis is confirmed. In May 2020, Mount Sinai Health System (U.S.) implemented artificial intelligence to analyze COVID-19 patients for rapid diagnosis based on CT scans and patient data. Thus, the advantages offered by AI technology have increased its adoption in medical diagnostics during the pandemic.

The AI in medical diagnostics market is segmented based on component, specialty, modality, end user, and geography. The study also evaluates industry competitors and analyzes the market at the country level.

Based on component, in 2022, the software segment is estimated to account for the largest share of the AI in medical diagnostics market. The large market share of this segment is attributed to the high demand for AI-based software solutions to deliver a quick and accurate medical diagnosis, the growing number of new software approvals & launches, and the rising shortage of specialists.

Based on specialty, in 2022, the radiology segment is estimated to account for the largest share of the AI in medical diagnostics market. The large market share of this segment is attributed to the growing demand for AI in medical imaging, increasing chronic disorders, an increasing number of new software products for AI in radiology, and the increasing global shortage of radiologists. In addition, the benefits of AI for radiologists in terms of non-interpretive data, such as reducing noise in medical images, creating high-quality images from lower doses of radiation, enhancing magnetic resonance image quality, and automatically assessing image quality, also supports the growth of this segment.

Based on modality, in 2022, the CT-scan segment is estimated to account for the largest share of the overall AI in medical diagnostics market. The large market share of this segment is attributed to the advantages that AI-based solutions offer, such as improved operational efficiency, reduced noise in medical images, and reduced patient backlogs and wait times. Additionally, the increasing patient pool prescribed for CT scans and growing numbers of products specific for CT scans supports the growth of this segment.

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Based on end user, in 2022, the hospitals segment is estimated to account for the largest share of the AI in medical diagnostics market. The large share of this segment is attributed to the increasing number of patients undergoing diagnostics procedures in hospitals, the robust financial capabilities of large hospitals to acquire high-cost AI-based software & services, the growing shortage of physicians, and the outbreak of the COVID-19 pandemic.

Based on geography, in 2022, North America is estimated to account for the largest share of the AI in medical diagnostics market, followed by Europe and Asia-Pacific. Some of the major factors driving the growth of the North American AI in medical diagnostics market include technological developments, increasing number of new product approvals, a high adoption rate of AI in healthcare, the presence of key market players, and established IT infrastructure in the healthcare sector. However, Asia-Pacific is slated to register the highest growth rate in the AI in medical diagnostics market during the forecast period. The high market growth in Asia-Pacific is attributed to the high growth opportunity due to the increasing prevalence of various chronic & infectious diseases, the increasing number of AI-based startups, especially in China and India, increasing funding, and a large potential of AI in addressing the gap in the healthcare infrastructure in the region

The report also includes an extensive assessment of the component, specialty, modality, end user, and geography, and key strategic developments adopted by leading market participants in the industry over the past four years (20192022). In recent years, the AI in medical diagnostics market has witnessed numerous product launches, approvals, agreements, collaborations, partnerships, and acquisitions.

The key players profiled in this market study are Siemens Healthineers AG (Germany), GE Healthcare (U.S.), Aidoc Medical Ltd. (Israel), International Business Machines Corporation (U.S.), AliveCor, Inc. (U.S.), VUNO Inc. (South Korea), Digital Diagnostics Inc. (U.S.), NovaSignal Corp. (U.S.), Riverain Technologies (U.S.), NANO-X IMAGING LTD (Israel), Imagen Technologies (U.S.), Koninklijke Philips N.V. (Netherlands), Agfa-Gevaert Group (Belgium), HeartFlow, Inc. (U.S.), and Arterys Inc. (U.S.).

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Scope of the Report:

Artificial Intelligence in Medical Diagnostics Market, by Component

Artificial Intelligence in Medical Diagnostics Market, by Specialty

Artificial Intelligence in Medical Diagnostics Market, by Modality

Artificial Intelligence in Medical Diagnostics Market, by End User

Artificial Intelligence in Medical Diagnostics Market, by Geography

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Artificial Intelligence in Medical Diagnostics Market Worth $9.38 Billion by 2029 - Exclusive Report by Meticulous Research - GlobeNewswire