Category Archives: Artificial Intelligence

A team of LSU researchers has developed a way to determine which drug therapies work best against an individual's unique type of cancer, possibly providing a way to find cures more quickly and make treatment more affordable.

The interdisciplinary team includes researchers from the School of Veterinary Medicine, College of Science, College of Engineering and the Center for Computation & Technology. It created CancerOmicsNet, a new drug discovery engine run by artificial intelligence.

Using algorithms originally designed to map complex social networks, like those utilized by Facebook, researchers generated three-dimensional graphs of molecular datasets that include cancer cell lines, drug compounds and interactions among proteins inside the human body.

The graphs are then analyzed and interconnected by AI, forming a much clearer picture of how a specific cancer would respond to a specific drug.

Dr. Michal Brylinski, associate professor of computational biology at LSU, said that the team used established datasets to train the CancerOmicsNet engine into using artificial intelligence.

"Once its trained, then you can ask for something that you dont know and this is the input data," he said. "So you ask what inhibitor you think is going to be effective against this cancer and then AI makes a prediction. Thats the implication to unseen data and then something like that goes to a wet lab and we can validate it.

Wet lab research was conducted by researchers at the LSU School of Veterinary Medicine and led by associate professor of research Brent Stanfield.

They developed the AI algorithm and everything, so our role in the study is just to be the practical applications of the technology," Stanfield said. "They developed the algorithm, identified the drugs and then we tested the drugs in our high-capacity systems to demonstrate their efficacy to kill cancer cells.

Researchers studied notoriously aggressive breast, prostate and pancreatic cell lines to train the AI to recognize connections between specific cancers and cancer drugs that control the production of the enzyme kinase within the body.

Kinase acts as a biological catalyst for cell communication and cell growth. Using drugs that lower kinase activity can suppress the growth of cancerous cells.

Brylinski said the research team used CancerOmicsNet to pick out six combinations of cancer cell lines with the drugs likely to be the most toxic to their gene expression profile and tested them, with encouraging results.

According to acceptable criteria, four out of six worked and this success rate is extremely high because if you just picked up six random drugs and say those drugs are going to work on this cancer, then theyre probably not going to work on that cancer," he said. "Four out of six was very encouraging and this is where we stand right now."

Using CancerOmicsNet like molecular speed dating, the AI can help researchers quickly match cancer cell lines with the drugs likely to be the most toxic to their growth and genetic profile.

Brylinski said knowledge gained through CancerOmicsNet can help overcome the challenge of determining how effective a particular kinase-inhibiting drug could be in the future.

The ultimate goal, he said, is to expand their research to potentially apply it in clinical settings.

"If we have a patient with a certain cancer, they can do a biopsy and then they can profile this cancer with respect to gene expression, genetic mutations and everything," Brylinski said. "Then they can input that data to CancerOmicsNet and it can suggest some therapy for this particular cancer and say this drug could be effective and 'another drug could not be effective.'

The effectiveness of various cancer drugs was initially believed to be tied to molecular consistency, the idea that cancer treatment should be targeted to a specific to a location in the body.

Michelle Collins, dean of the College of Nursing and Health at Loyola University New Orleans and a scientist not involved in the LSU research, said CancerOmicsNet is an example of how our current medical understanding of cancer treatment meets advances in genetic studies and AI.

When cancer drugs first came out, they were one size fits all and werent really tailored to the individual and so you see the medications work better on some people than others," she said. "And with the advent of genetics and genomics, which are the future of medicine, were now going to be able to tailor treatments to the patient and not just in oncology.

Collins said she sees CancerOmicsNet being extremely beneficial to oncological studies and treatment in the future.

I think it has the potential to really revolutionize the field of oncology, because well be able to treat people with medication that is more timely tailored to them," she said. "All of that is good if youre a patient with cancer.

Brylinksi said that the ability to treat cancer with a more direct, focused clinical approach makes him excited to see how CancerOmicsNet develops over time.

"I dont know if were going to make some major breakthrough in oncology any time soon, but were contributing pieces where if enough people are doing this, the whole field is moving forward towards the goal of improving human health," he said. "Were very happy that we can make some contribution, which might not be a huge breakthrough down the road, but definitely something that is useful to improving human health and thats really cool actually.

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Can artificial intelligence help identify best treatments for cancers? LSU researchers say yes - The Advocate

TheAmazonVirginia Tech Initiative for Efficient and Robust Machine Learninghas announced support for two Amazon Fellows and four innovative research projects led by Virginia Tech faculty that further the initiatives mission of advancing new directions in machine learning.

Funded by Amazon, housed in theCollege of Engineering,and directed by researchers at theSanghani Centerfor Artificial Intelligence and Data Analyticson Virginia Techs campus in Blacksburg and at the Innovation Campus in Alexandria, the initiative was launched in March to support student and faculty-led development and implementation of innovative approaches to robust machine learning such as ensuringthat algorithms and models are resistant to errors and adversaries that could address worldwide industry-focused problems.

An open call for student fellowship nominations and research projects went out concurrently across the Virginia Tech campuses. The initiatives advisory committee, comprised of Virginia Tech faculty and Amazon researchers, selected two Amazon Fellows from among 11 nominations and four faculty award recipients from 14 submissions.

Our inaugural cohort of fellows and faculty-led projects showcases the breadth of machine learning research happening at Virginia Tech, saidNaren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the Amazon-Virginia Tech Initiative. The areas represented include federated learning, meta-learning, leakage from machine learning models, and conversational interfaces.

This research will not only contribute to new algorithmic advances, but also study issues pertaining to practical and safe deployment of machine learning, said Ramakrishnan who also directs theSanghani Centerfor Artificial Intelligence and Data Analytics. We are very excited that the partnership between Amazon and Virginia Tech has enabled these projects.

"The talent and depth of scientific knowledge at Virginia Tech is reflected in the high-quality research proposals and Ph.D. student fellowship applications we have received, said Prem Natarajan, vice president of Alexa AI. I am excited about the new insights and advances in robust machine learning that will result from the work of the faculty and students who are contributing to this initiative."

The Amazon Fellows are:

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Amazon Fellows and faculty-led projects advance innovations in machine learning and artificial intelligence - Virginia Tech Daily

IRVINE, Calif.--(BUSINESS WIRE)--Genomic Testing Cooperative, LCA (GTC) announced that its innovative artificial intelligence (AI) algorithms are now formally implemented in daily use to aid pathologists in the diagnosis and interpretation of molecular findings of genomic profiling.

GTCs RNAnalysis algorithm is used to distinguish between 45 different diagnostic classes providing probability scores. This algorithm is complemented by a second algorithm called TraceWork. When needed, TraceWork is used to distinguish between two diagnostic entities determined by RNAnalysis to be of similar high probability score.

Results of validation of these algorithms are now published in The American Journal of Pathology, a part of Elsevier's Journal Network (DOI:https://doi.org/10.1016/j.ajpath.2022.09.006). For example, independent blind testing of RNAnalysis algorithm showed correct first-choice diagnosis in 100% of acute lymphoblastic leukemia, 88% of acute myeloid leukemia, 85% of diffuse large B-cell lymphoma, 82% of colorectal cancer, 49% of lung cancer, 88% of chronic lymphocytic leukemia and 72% of follicular lymphoma. The TraceWork algorithm distinguished between lung cancer and colorectal cancer with 97.2% sensitivity and 94.5% specificity, between Hodgkin lymphoma and normal lymph node with 95.4% sensitivity and 100% specificity, between follicular lymphoma and diffuse large B-cell lymphoma with 95.9% sensitivity and 93.1% specificity, and between breast cancer and ovarian cancer with 100% sensitivity and 94.2% specificity.

The information provided by these algorithms are used in the context of clinical and other molecular and pathologic findings and not meant to replace the need for physicians clinical decision, said Dr. Maher Albitar, founder, chief medical officer, and chief executive officer of GTC. We believe that transcriptomic data when combined with AI provides an efficient and effective information that can replace the need for large number immunohistochemical staining and flow cytometry testing, especially when tissue samples are scant, Dr. Albitar added.

Dr. Andre Goy, Chairman & Chief Physician Officer at John Theurer Cancer Center and Academic Chairman of Oncology at Hackensack Meridian School of Medicine, stated, Precision diagnosis is extremely important for the practice of precision medicine. Todays RNA and DNA profiling generates big data that requires sophisticated algorithms to decipher the clinical relevance of this data. GTCs molecular profiling and algorithms had helped us resolve numerous diagnostically challenging cases and the results made a difference in patients management and outcome.

Dr. Aamir Ehsan, CEO/ President, Medical Director and board-certified hematopathologist and molecular geneticist of CorePath laboratories, at San Antonio, Texas, who is a collaborator and coauthor on the published work, said, Unlike AI and imaging, transcriptomic data and AI incorporates immunohistochemistry and flow cytometry data as well as numerous additional biomarkers, but more importantly allows us to look at each biomarker individually to make the final pathologic decision. This represents major advances in the practice of pathology.

It is estimated that approximately 10% of all cancer cases are misdiagnosed and 4% of solid tumors are presented as cancer of unknown primary CUP.

About Genomic Testing Cooperative, LCA

Genomic Testing Cooperative (GTC) is a privately-owned molecular testing company located in Irvine, CA. The company operates based on a cooperative (co-op) business model. Members of the co-op hold type A shares with voting rights. The company offers its patron members a full suite of comprehensive genomic profiling based mainly on next generation sequencing. Molecular alterations are identified based on rigorous testing with the aid of specially developed algorithms to increase accuracy and efficiency. The clinical relevance of the detected alterations is pulled from numerous databases using internally developed software. Relevance of findings to diagnosis, prognosis, selecting therapy, and predicting outcome are reported to members. The co-op model allows GTC to make the testing and information platform available to members at a lower cost because of a lower overhead. For more information, please visit https://genomictestingcooperative.com/.

Forward Looking Statements

All of the statements, expectations and assumptions contained in this press release are forward-looking statements. Such forward-looking statements are based on the GTC managements current expectations and includes statements regarding the value of comprehensive genomic profiling, RNA profiling, DNA profiling, algorithms, therapy, the ability of testing to provide clinically useful information. All information in this press release is as of the date of the release, and GTC undertakes no duty to update this information unless required by law.

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Genomic Testing Cooperative and its Co-Op Members First to Use Artificial Intelligence to Distinguish Between 45 Hematologic and Solid Neoplasms Using...

In this article, John Bohlmann, founder and CEO, HawkenQA, dives into smart building technology, how it works, and how AI has empowered the tech to go mainstream while cutting costs, automating operations, and creating a healthy environment that impacts carbon emissions.

When most people think about carbon emissions, they think about rush hour traffic jams and industrial activities. Many experts urge people to look closer to home when considering how to make a greener world. However, few imagine that the building where they live or work is what experts are referring to.

The United Nations Environment Programme (UNEP) explains that buildings are responsible for one-third of global greenhouse gas emissions, using 40% of global energy, and the sector is the largest contributor to carbon emissions.

But how can buildings have such an impact on the global climate? The answer is simple: operation and construction. In operation, emissions can be constant. These are driven by electricity use, heating, ventilation, air conditioning (HVAC), and equipment. Buildings also have an embodied carbon, which refers to the carbon footprint generated during construction. This carbon factor includes everything from running the construction equipment to the carbon emissions generated through the supply chain, for example, when sourcing steel and concrete.

See More: Avoid Flightmares with AI-enabled Virtual Agents

Despite these severe impacts, buildings have one of the most significant potentials for green transformation. Today, data-driven technology empowered by machine learning models or artificial intelligence (AI) creates smart buildings where the software automatically integrates with the buildings components. HVAC, air quality, temperature, energy use, occupancy, downtime hours, ventilation, and many other factors can be continually monitored with sensors paired with monitoring technology and can make automated decisions to optimize performance.

Installing smart building platforms was challenging for years because every building is different. Technology has tackled this problem using AI, which can reduce the time and cost of installation. AI-based building solutions are a big step in the right direction toward reducing carbon emissions, not only in the U.S. but globally. Today, smart building platforms are more advanced, more affordable, easier to install, and easier to use than ever.

Reducing the carbon emissions of a building doesnt mean turning off the lights. It means optimizing resources like airflow, electricity, and water or installing solar panels. Indoor air quality technology is essential to a low carbon footprint due to the significant energy consumption that HVAC systems have. Indoor air monitoring technology uses AI to intelligently control the HVAC-energy balance without compromising the comfort and health of the people inside a building.

AI learns the habits of the occupants of a building and can predict when to increase, stabilize, or decrease the usage of heating and air conditioning systems. Additionally, these new systems can be space-specific, only applying changes in the rooms or common areas needed. The same AI can optimize air quality in the building by revealing and acting on sources of indoor air pollution like CO2, humidity, or high particulate matter.

AI can also improve operations and maintenance. When smart building systems are integrated with smart devices or the IoT, the AI will detect any abnormality in a device. For example, if a heating device malfunctions, it will use less or more energy and affect the temperature of the room. Therefore, the AI can identify if a device is having a problem, make adjustments, and notify management. Smart AI systems can also manage scheduled HVAC filter changes or other maintenance and review and approve work orders.

The automation processes not only eliminate human error from the equation but also save building owners a significant amount of labor, energy spending, and work costs that were previously done manually.

New trends in reducing carbon emissions in the building construction sector include using alternative green materials or sourcing materials from low-carbon producers. Additionally, architects and engineers are integrating solar, wind, and alternative renewable energy sources into construction to maximize the buildings operations and reduce their impact on the grid.

On the other hand, during construction, its essential to maximize natural resources through innovative ventilation mechanisms and manage temperature by using windows to keep the sunlight in during cold seasons and keep it out during summertime.

Decision makers should be following new developments closely in smart buildings due to the many benefits they provide. Smart buildings can bring down carbon emissions, improve performance, build a good reputation, and reduce liability.

Next-generation smart building platforms also enable radically better financial outcomes for real estate managers. Green buildings rents and sale prices are higher, and vacancy rates are significantly lower. On the other hand, smart-AI technology can have direct economic savings. Typically businesses pay $1 per square foot per month on energy, $10/sqft/month on office rent, and $100/sqft/month on employee salaries.

But leaders investing in smart-AI building tech are flipping this economic equation for buildings while adding significant wellness and health value and creating better green workplaces.

According to the U.S. Department of Energy, smart building technology can reduce energy by more than 60% in residential buildings and up to 59% in commercial buildings. Additionally, investing in AI smart building technology that provides good indoor air quality standards has proven to improve employee productivity and cognitive function in office buildings by more than 10%.

See More: From raw data to ML models: The magic behind AI-powered feature engineering

Deloitte Insights, an international professional services network, explains that smart buildings marry physical assets with the digital fabric that connects spaces. The organization explains that leaders shouldnt spend money on smart building technology without having a clear strategy. Before installing smart building technology, decision-makers should draft a solid business case outlining the benefits and savings, goals, revisions, and milestones.

Additionally, smart building technology should factor in technical needs (energy savings) and consider human elements such as wellness, comfort, health, and performance. AI is undoubtedly an innovation disruption, and data is the raw material that drives it. However, data governance and data management are essential. Buildings should never collect data without identifying what it will be used for. Finally, choosing a flexible technology that will allow users to install updates and keep up with modern trends is essential.

Have you seen smart building solutions in action lately? Tell us about it on Facebook, Twitter, and LinkedIn. Wed love to know!

Image Source: Shutterstock

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Reducing Carbon Emissions With AI And Smart Building Technology - Spiceworks News and Insights

About Focal Systems:

Founded in 2015 in San Francisco out of Stanfords Computer Vision Lab, Focal Systems is the industry leader in retail automation. Our mission is to automate and optimize brick and mortar retail with state-of-the-art deep learning and AI. We have pioneered the worlds first Self-Driving Store- an OS that revolutionizes how stores are run. Focal has raised more than $40M to date and scaled solutions on three continents in hundreds of stores, with over 100,000 cameras deployed. Learn more at: https://focal.systems.

Contact:

Lizzy Harris for Focal Systems

[emailprotected]

1-303-503-1136

About Piggly Wiggly:

Building on its more than 100-year history in the grocery business, Piggly Wiggly continues to grow its presence with stores throughout the Midwest, South and Northeast. C&S Wholesale Grocers, Inc. operates corporate stores and services independent franchisees under a chain-style model. This unique grocery store offers the selection and assortment of a national chain, with the service and local customization of a community-based retailer. Each store contains specialized local assortments to meet local shoppers needs.

Piggly Wiggly Midwest: https://www.shopthepig.com/

Piggly Wiggly Carolinas: https://www.thepig.net/

About C&S Wholesale Grocers, Inc. C&S Wholesale Grocers, Inc. is an industry leader in supply chain solutions and wholesale grocery supply in the United States. Founded in 1918 as a supplier to independent grocery stores, C&S now services customers of all sizes, supplying more than 7,500 independent supermarkets, chain stores, military bases and institutions with over 100,000 different products. We are an engaged corporate citizen, supporting causes that positively impact our communities. To learn more, please visit http://www.cswg.com.

C&S Media Contact:

Lauren La Bruno

Vice President of Communications, Change Management and Community Relations

C&S Wholesale Grocers, Inc.

[emailprotected]

C&S Investor Relations:

Julie Drake

Vice President, Assistant Treasurer

C&S Wholesale Grocers, Inc.

[emailprotected]

Piggly Wiggly Marketing Contact:

Molly Rippinger

Director, Marketing

Piggly Wiggly Midwest

[emailprotected]

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Piggly Wiggly Midwest Partners with Focal Systems to Leverage Artificial Intelligence and Automation to Transform the Shopper Experience - Grocery...

MSPs, Edge Providers, and Large Enterprises will find CloudFabrix's domain-agnostic, distributed, Robotic Data Automation fabric-based AIOps model appealing

PLEASANTON, Calif., Oct. 13, 2022 /PRNewswire/ -- CloudFabrix the inventor of Robotic Data Automation Fabric Platform and the Data-centric AIOps Leader, was named a Leader and Innovator in the 2022 Gigaom Radar for AIOps report for a 2nd consecutive year. The report evaluated 24 major AIOps vendors across Key criteria and evaluation metrics that should be applied when selecting an AIOps solution. The report segregates the 24 vendors across Leaders, Challengers, and New Entrants. CloudFabrix was identified as a Leader, Fast Mover, and Innovator that provides a complete AIOps solution, with a unique Robotic Data Automation Fabric and Distributed AIOps model, including for AIOps at the edge.

According to the report, "This year proved to be one of explosive growth in AIOps tooling and solutions. In some cases, AIOps functionality was achieved by bolting an artificial intelligence and machine learning (AI/ML) engine to existing software, via acquisition or internal development, and marketing it as an AIOps solution. Other vendors built entire platforms around homegrown or acquired AI/ML, jumping into a crowded arena competing with pure AI/ML solutions and platform tools." CloudFabrix's platform is homegrown, built with microservices, is cloud native and can run entirely in the cloud, in a hybrid deployment, or on-premises.

This year's report points out one key differentiation, among the 24 surveyed vendors dividing them into domain-agnostic and platform solutions and what it means for end users. The domain-agnostic solutions can be added to any environment with minimal interruption to the business, while platforms may require the displacement of several existing monitoring solutions.

CloudFabrix scored high ranks across the 3 categories, as identified by the report -

The report identifies CloudFabrix's key capabilities -

Supporting Quotes

"CloudFabrix continues to impress us with its innovation and its ability. They have shown a pulse on the AIOps market and a quest to constantly improvise in the areas where they were at a disadvantage, across the 2 years we have evaluated them. We are hopeful they continue on this path as Digital Transformation and AIOps are becoming mainstream for enterprises," said Ron Williams, Principal Analyst, Gigaom.

Shailesh Manjrekar, Vice President of AI and Marketing, CloudFabrix said, "We are delighted and honored to be recognized as a leader by Gigaom for 2 consecutive years. This endorses us as a "Fast Mover," demonstrated by our recent launch of "Persona-based Composable Analytics for AIOps." He further asserted, "Our success and scalability are demonstrated by our recent wins with large global MSPs and Enterprises. We continue to strive to delight our customers and make their autonomous enterprise journey, a reality by democratizing Data-First, AI-First, and Automation everywhere strategies."

Resources:

About CloudFabrix

CloudFabrix is the leading Data-centric AIOps Platform vendor and the inventor of Robotic Data Automation Fabric (RDAF). RDAF delivers integrated, enriched and actionable data pipelines to operational and analytical systems. RDAF unifies Observability, AIOps and Automation for Operational Systems and enriches analytical systems. CloudFabrix empowers Business and IT leaders with AI-powered actionable intelligence to make faster and better decisions and accelerate IT planning and Autonomous operations. For more information, visit cloudfabrix.com

Media Contact / Press Enquiry:Shailesh Manjrekar[emailprotected]408-421-4214

SOURCE CloudFabrix

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CloudFabrix Was Named a Leader and Innovator in the 2022 Gigaom Radar for Artificial Intelligence for Operations (AIOPs) - PR Newswire

Artificial intelligence: its one of those mind-bending subjects that provokes fascination yet is misunderstood by almost everyone (myself included). If you bring it up in the pub to sound brainy, the conversation might only go as far as Alan Turing, Deep Blue versus Garry Kasparov and perhaps the new series of BBC drama The Capture, in which foreign spies seem to use AI to manipulate CCTV and live broadcasts.

But AI has long moved beyond the realms of gripping fiction and headline-grabbing experiments into the real world. For instance, DeepMind, a British subsidiary of Alphabet, recently released almost the entire make-up of the protein universe, mapped for the first time by its AI programme AlphaFold. This data is already being used to advance our understanding of life-threatening illnesses and help protect honeybees.

Amid all the scaremongering about AI, perhaps we should be more aware of these positive developments, particularly if were investors. Funding technology that is tangibly solving the worlds trickiest problems surely thats responsible investing at its finest?

Besides, AI applications that drive efficiency, reduce costs, serve vital needs, and cement a companys competitive advantage should generate significant value. Thats not to be sniffed at in this current investing climate.

The Sanlam Artificial Intelligence Fund has specifically hit on AI as a way to back winners in the stock market. Its managers Chris Ford and Tim Day marked the funds fifth anniversary recently by sharing some intriguing insights on how AI has improved in recent times.

Things that people called pipe dreams when we launched the fund in 2017 are now happening, says Chris Ford. What was impossible is now possible.

Examples include Alibabas equivalent scoring better than humans in Stamford Universitys reading and comprehension test, and the Canadian platform Blue Dot spotting the spread of Covid-19 nine days before the World Health Organisation put the alert out.

How has this translated into hard financial returns? The 715.5m fund has had an annualised return of 18.2 per cent since it was established, which is respectable, and its top performers have included household names like Tesla, Netflix, Microsoft and Ocado, alongside lesser-known AI innovators like NVIDIA, Zendesk, and Appen.

The managers even have their own AI application (Orbit) to help them stay across things. Its ongoing charges figure is 0.8 per cent not dirt cheap, but not bad for an active fund.

Do actively managed thematic funds like this have the edge, not just over their cheaper rivals but within the broader investment universe?

Sanlams fund, a traditional open-ended unit trust, has comfortably beaten the MSCI World index, which has had an annualised return of 8.41 per cent over the past five years. By contrast, only 39 per cent of thematic exchange-traded funds (ETFs) have survived and outperformed the index over that same period, according to data from Morningstar.

But the funds performance has markedly dipped in recent times, as the global rout in stock markets hits technology shares particularly hard. This is the downside of thematic investing: clever use of AI may not be the main reason why a company flourishes, or why investors feel well-disposed towards it.

The likes of Tesla, Ocado and Netflix can end up in choppy waters when investing conditions change, and it becomes clear that these firms have been overvalued by the market for what they do. Thematic investing can also lead to a good deal of overlap in your portfolio, with the same names cropping up again and again. This can undermine diversification and add to your risks.

Also, whilst many of the Sanlam holdings are doing great work (like Alphabets DeepMind), there is a good reason why its not classed as socially responsible. EthicsGrade is a ratings agency specialising in understanding the risks stemming from digitalisation, particularly AI, and it has awarded a top A rating to only a handful of firms, including Microsoft and Deutsche Telekom.

Fewer than a third of the 302 major companies it graded have a score of C or above. Too many were unrated altogether because they either ignored or provided little to no details on governance and ethics policies relating to AI.

Experts mostly agree that the more sinister AI possibilities as depicted in The Capture still look far-fetched. But its worrying that many of the major companies we buy from and invest in are being cavalier regarding privacy breaches, data misuse, racial and gender discrimination, the creation of harmful weapons and other potential dangers of AI.

AI is not going anywhere (particularly since the Chinese government is investing heavily in AI research and development). If it proves as beneficial for its first movers as many believe, AI could become the first big success story of so-called thematic investing, previously dismissed as no more than a trendy gimmick that promises more than it can deliver.

The big question is whether this technology will be treated with enough care by its creators and investors. If not, disappointing returns will be the least of our problems.

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Investing in artificial intelligence: The pros and cons - iNews

There are many examples that could be given to demonstrate the benefits of technology and Artificial Intelligence in supporting the rehabilitation of people who have health issues.

To start with, there is the project being carried out in the intensive care unit (ICU) of the Hospital del Mar in Barcelona, where people admitted to hospital can start their recovery by taking a ride on a bicycle without actually leaving their bed, in a process that combines physical exercise and virtual reality.

This idea consists of an ergometric bicycle that allows bedridden patients to do physical activity similar to cycling, combined with virtual reality to start their rehabilitation process in an early and enjoyable manner.

The system allows the patient to pedal even when lying in bed and adapts to their physical capacity, even having the capacity to have them move their legs passively and thus mobilising the muscles when the patient does not yet have the capacity to do so themselves.

In addition, the bicycle is combined with a 360-degree reality glasses system, which allows patients to feel as if they were actually cycling through different areas of the city of Barcelona.

To do this, they work with the Barcelona-based company Realitytelling, which produces the video capsules that patients watch while they exercise, making it necessary to synchronise the video with the rhythm of pedalling to make the experience more satisfactory.

New technologies are also helping to alleviate as far as possible the effects of degenerative diseases such as Alzheimers, Parkinsons or others, such as Amyotrophic Lateral Sclerosis (ALS).

In the case of Parkinsons disease, work is under way on a new brain stimulation treatment that could improve quality of life and help control symptoms in people with advanced cases of Parkinsons.

Research, conducted by the University of Florida in conjunction with fourteen medical centres, tested the effectiveness and safety of a device that helps improve symptoms through deep brain stimulation (DBS).

The device aims to reduce tremors, improve the slowness of movement, reduce motor disability and involuntary movements related to the disease and the drugs used to treat it.

To do this, the system works through small electrodes implanted in the brain connected to the device which is programmed to emit a mild electrical current.

Artificial Intelligence takes centre stage in a European project involving researchers from the University of Castilla-La Mancha (UCLM), through the Faculty of Physiotherapy and Nursing, for use in the functional diagnosis of children with hemiparesis (paralysis on one side of the body) and also the construction of home rehabilitation systems (telerehabilitation).

This is the European project AInCP (Artificial Intelligence in Cerebral Palsy), led by the Italian University of Pisa, which will clinically validate new Artificial Intelligence algorithms to develop clinical tools that support evidence-based decisions in the functional diagnosis of children with hemiparesis and create rehabilitation systems that they can access at home.

Several Spanish universities are collaborating on a project in which they seek to improve the behaviour and response of children with autism spectrum disorders (ASD) by means of social robots.

This multidisciplinary work involves experts in paediatrics, biomedical engineering, robotics and neurorehabilitation to try to offer personalised solutions and support systems to professionals working in this field.

To this end, work is being done with aspects associated with biomedical engineering such as advanced sensors for physiological signals, artificial intelligence techniques and new human-like robots capable of teaching these children the meaning of different expressions and emotions, in what could be called personalised rehabilitation.

This technique is based on the application of knowledge acquired in the field of electrodermal activity sensors and heart rate variability or cameras for the detection of gestures and emotions in faces.

One of the cases that first comes to mind when we think of how technology can help in the arduous path of patient rehabilitation, whether due to an illness or an accident, is that of those who are unable to move due to a spinal cord injury.

Last February, the journal Nature Medicine reported the results of a Swiss team of researchers, part of an ongoing clinical trial, in which three people who had suffered a complete spinal cord injury and were paraplegic are now able to walk.

All this is possible thanks to an implant that stimulates the area of the spinal cord that controls the muscles of the trunk and legs, which works from an application that incorporates artificial intelligence.

These soft implants were placed under the vertebrae in contact with the spinal cord and are capable of modulating the neurons that regulate the activity of precise muscle groups, says neuroscientist Grgoire Courtine of the Ecole Polytechnique Fdrale de Lausanne (EPFL).

In this way, he adds, the spinal cord can be activated as the brain would naturally do for standing, walking, cycling or swimming.

The researchers combined this technology with a personalised computational framework to position the electrode palette to the needs of each patient, and thus personalise the activity stimulation programmes.

To better illustrate this process, we refer to one of the patients who received this technique, Michel Roccati, an Italian who four years ago had a motorbike accident and became completely paraplegic. Michel can now get up and walk using a walker in which two small remote controls are inserted.

A tablet sends the stimulation commands to a pacemaker in Michels abdomen, from which the stimuli are transmitted to the spinal implant to make Michel stand up.

The way the system works means that with a press of the button on the right side of his walker plus his willingness to activate his muscles, his left leg flexes and then positions a few centimetres further forward. When the left button is activated, the right leg takes a step in turn and starts to walk.

This system has also enabled him to go up and down stairs.

Another example of technology taking a decisive role in the peoples rehabilitation is the use of a bionic chip to enable an 88-year-old British woman who had lost the sight in her left eye to detect signals in that eye again.

The patient, a mother of seven and grandmother of eight, suffers from geographic atrophy, the most common form of age-related macular degeneration (AMD), a disease affecting more than five million people worldwide.

The operation, performed at Londons specialist Moorfields Eye Hospital, involved the surgical insertion of a two-millimetre microchip into the centre of the patients retina. The patient had to wear special glasses containing a video camera linked to a small computer, which in turn was attached to a waist band.

The chip captures the image provided by the glasses and transmits it to the computer, which uses artificial intelligence algorithms to process the information and guide the focus of the glasses.

Finally, the glasses project this image as an infra-red beam through the eye to the chip, which transforms it into an electrical signal that travels back through the cells of the retina to the brain. The latter, in turn, interprets this signal as if it were natural vision.

Just as we have been seeing during the COVID-19 pandemic all that technological tools and Artificial Intelligence are doing in health, their use in the field of patient rehabilitation is also meaning a step forward in improving peoples living conditions.

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Technology and Artificial Intelligence in support of rehabilitation - Telefnica

The Netherlands will host an international summit on the responsible application of artificial intelligence in the military domain in The Hague in February 2023.

The aim of the conference is to establish an agenda that will ultimately lead to international agreements on how to develop and apply this technology responsibly in this domain.

This international summit on Responsible AI in the Military Domain REAIM 2023 will be hosted in collaboration with the Netherlands Ministry of Defence at the World Forum in The Hague on 15 and 16 February.

The announcement was made at the United Nations General Assembly in New York by Minister of Foreign Affairs, Wopke Hoekstra.

Artificial intelligence (AI) is essential for high-tech, future-proof armed forces. At the same time, there are risks and dilemmas associated with it, for instance if the deployment of AI leads to lack of human control, the potential escalation of violence or lack of clarity as regards responsibility.

According to Mr. Hoekstra, AI is developing at a rapid pace, including in the military domain. This new technology is set to become one of the greatest challenges we will face in the area of international security and arms control. If we dont make international agreements now about frameworks for the responsible development, deployment and use of AI, we will regret it later.

The event will bring together representatives of governments, knowledge institutions, industry, thinktanks and civil society organisations from all over the world. In order to allow as many people as possible from these target groups to take part in the summit, some of the sessions will be streamed online. To launch the summit, anonline talk showwas held at the United Nations Headquarters in New York with Mr. Hoekstra taking part, alongside United Nations Under-Secretary-General and High Representative for Disarmament Affairs, Ms. Izumi Nakamitsu.

Bas Schot, Head of Hague Convention Bureau adds: Once again The Hague is hosting a summit that will lead to the long-term benefit of all. As the international city of peace and justice, it is only fitting we host a summit on the responsible use of arms and I look forward to seeing the legacy this event will bring.

Marije Bouwman, Director of Operations, Safety & Security, World Forum The Haguesaid,World Forum The Hague is honored to host this conference. We are thankful we can continue to build on our expertise of high-level secured events.

Image shows Minister Hoekstra with representatives from, amongst others, Chile, the United States, United Kingdom, France, South Korea, Germany, Turkey, Canada, Mexico, Australia, Indonesia at the launch of the REAIM 2023 summit in The Hague

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The Hague to Host Summit on Artificial Intelligence in the Military Domain - FTNnews.com

What GAO Found

Several machine learning (ML) technologies are available in the U.S. to assist with the diagnostic process. The resulting benefits include earlier detection of diseases; more consistent analysis of medical data; and increased access to care, particularly for underserved populations. GAO identified a variety of ML-based technologies for five selected diseases certain cancers, diabetic retinopathy, Alzheimers disease, heart disease, and COVID-19 with most technologies relying on data from imaging such as x-rays or magnetic resonance imaging (MRI). However, these ML technologies have generally not been widely adopted.

Academic, government, and private sector researchers are working to expand the capabilities of ML-based medical diagnostic technologies. In addition, GAO identified three broader emerging approachesautonomous, adaptive, and consumer-oriented ML-diagnosticsthat can be applied to diagnose a variety of diseases. These advances could enhance medical professionals capabilities and improve patient treatments but also have certain limitations. For example, adaptive technologies may improve accuracy by incorporating additional data to update themselves, but automatic incorporation of low-quality data may lead to inconsistent or poorer algorithmic performance.

Spectrum of adaptive algorithms

We identified several challenges affecting the development and adoption of ML in medical diagnostics:

These challenges affect various stakeholders including technology developers, medical providers, and patients, and may slow the development and adoption of these technologies.

GAO developed three policy options that could help address these challenges or enhance the benefits of ML diagnostic technologies. These policy options identify possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry. See below for a summary of the policy options and relevant opportunities and considerations.

Policy Options to Help Address Challenges or Enhance Benefits of ML Diagnostic Technologies

Evaluation (reportpage 28)

Policymakers could create incentives, guidance, or policies to encourage or require the evaluation of ML diagnostic technologies across a range of deployment conditions and demographics representative of the intended use.

This policy option could help address the challenge of demonstrating real world performance.

Data Access (reportpage 29)

Policymakers could develop or expand access to high-quality medical data to develop and test ML medical diagnostic technologies. Examples include standards for collecting and sharing data, creating data commons, or using incentives to encourage data sharing.

This policy option could help address the challenge of demonstrating real world performance.

Collaboration (reportpage 30)

Policymakers could promote collaboration among developers, providers, and regulators in the development and adoption of ML diagnostic technologies. For example, policymakers could convene multidisciplinary experts together in the design and development of these technologies through workshops and conferences.

This policy option could help address the challenges of meeting medical needs and addressing regulatory gaps.

Source: GAO. | GAO-22-104629

Diagnostic errors affect more than 12 million Americans each year, with aggregate costs likely in excess of $100 billion, according to a report by the Society to Improve Diagnosis in Medicine. ML, a subfield of artificial intelligence, has emerged as a powerful tool for solving complex problems in diverse domains, including medical diagnostics. However, challenges to the development and use of machine learning technologies in medical diagnostics raise technological, economic, and regulatory questions.

GAO was asked to conduct a technology assessment on the current and emerging uses of machine learning in medical diagnostics, as well as the challenges and policy implications of these technologies. This report discusses (1) currently available ML medical diagnostic technologies for five selected diseases, (2) emerging ML medical diagnostic technologies, (3) challenges affecting the development and adoption of ML technologies for medical diagnosis, and (4) policy options to help address these challenges.

GAO assessed available and emerging ML technologies; interviewed stakeholders from government, industry, and academia; convened a meeting of experts in collaboration with the National Academy of Medicine; and reviewed reports and scientific literature. GAO is identifying policy options in this report.

For more information, contact Karen L. Howard at (202) 512-6888 or howardk@gao.gov.

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Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics - Government Accountability...