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LONDON, Sept. 14, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the digital transformation market, the increasing adoption of machine learning and artificial intelligence is expected to drive the growth of the digital transformation market going forward. Digital transformation provides traditional businesses with solutions like cloud computing, big data & analytics, data management, and other advanced features such as artificial intelligence and machine learning, which help in the optimization of business operations, leading to reduced efforts in operations and increased efficiency. Thus, their usage increased in various sectors such as healthcare, banking, transportation, manufacturing, and others, increasing the demand in the digital transformation market.

For instance, according to the report published by Cloudmantra, an India-based technology services company, the usage of machine learning in the Indian manufacturing industry has increased manufacturing capacity by up to 20% while reducing material usage by 4% in 2021. It also gives manufacturers the ability to control Overall Equipment Effectiveness (OEE) at the plant level, increasing OEE performance from 65% to 85%. Furthermore, according to the MIT Technology Review Insights report in 2022, approximately 60% of manufacturers are using artificial intelligence to improve daily operations, design products, and plan their future operations. Therefore, the rising adoption of machine learning and AI drives the digital transformation market.

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The global digital transformation market size is expected to grow from $0.94 trillion in 2021 to $1.17 trillion in 2022 at a compound annual growth rate (CAGR) of 24.7%. The global digital transformation market share is expected to grow to $2.64 trillion in 2026 at a CAGR of 22.4%.

Technological advancement in digital solutions is gaining popularity among the digital transformation market trends. Major companies operating in the digital transformation market are focused on developing technologically advanced products to strengthen their market position. For instance, in April 2020, Oracle Corporation, a US-based computer technology corporation and software solutions provider, built a new cloud data storage service called GoldenGate, an oracles cloud infrastructure software that uses real-time data analytics for the analysis of data. Real-time data analysis provides a very quick analysis of data by using different logical and mathematical operations, which helps in understanding business requirements and implementing any decision instantly. GoldenGate provides clients with a highly automated and fully managed cloud service such as database replication, analyzing real-time data, and real-time data ingestion to the cloud, which will make daily business operations easy and analyzable.

Major players in the digital transformation market are Microsoft Corporation, IBM Corporation, Oracle Corporation, Google Inc., Cognizant, Accenture PLC, Dell EMC, Siemens AG, Hewlett-Packard Company, Adobe Systems Inc., Capgemini, Cognex Corporation, Deloitte, Marlabs Inc., Equinix Inc., PricewaterhouseCoopers, Apple Inc., Broadcom, CA Technologies, KELLTON TECH, International Business Machines Corporation, Hakuna Matata Solutions, ScienceSoft Inc., SumatoSoft, Space-O Technologies, HCL Technologies, and Tibco Software Inc.

The global digital transformation market analysis is segmented by technology into cloud computing, big data and analytics, artificial intelligence (AI), internet of things (IoT), blockchain; by deployment mode into cloud, on-premises; by organization size into large enterprises, small and medium-sized enterprises (SMEs); by end-user into BFSI, healthcare, telecom and IT, automotive, education, retail and consumer goods, media and entertainment manufacturing, government, others.

North America was the largest region in the digital transformation market in 2021. Asia-Pacific is expected to be the fastest-growing region in the global digital transformation market during the forecast period. The regions covered in the global digital transformation industry outlook are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.

Digital Transformation Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide digital transformation market overviews, analyze and forecast market size and growth for the whole market, digital transformation market segments and geographies, digital transformation market trends, digital transformation market drivers, digital transformation market restraints, digital transformation market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.

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Artificial Intelligence Global Market Report 2022 By Offering (Hardware, Software, Services), By Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision, Others (Image Processing, Speech Recognition)), By End-User Industry (Healthcare, Automotive, Agriculture, Retail, Marketing, Telecommunication, Defense, Aerospace, Media & Entertainment) Market Size, Trends, And Global Forecast 2022-2026

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The Increased Use Of Machine Learning And Artificial Intelligence Is Expected To Fuel The Digital Transformation Market As Per The Business Research...

South Korea, Seoul, Sept. 16, 2022 (GLOBE NEWSWIRE) -- As per a new report by Fact.MR, a market research and competitive intelligence provider, the global demand for neurology devices is anticipated to reach a valuation of US$ 13 billion by 2027.

The prevalence of several neurological conditions as well as cerebrovascular disorders such as stroke, migraine, and headache is increasing across the world, which is driving up demand for neurology devices. Growing preference for minimally-invasive procedures and ongoing innovations in neurology equipment are also increasing market value.

With the support of non-invasive, image-guided neurology technologies, neurovascular issues such as tumors, cavernous malformations, and stroke are addressed. The market for neurology devices is expanding as a result of many key variables, including an increase in head traumas, high rates of stress in young people, an increase in the geriatric population, and a large patient base.

Get Sample PDF of This Research Report with Detailed Table of Content:

https://www.factmr.com/connectus/sample?flag=S&rep_id=7702

Rising patient awareness regarding the availability of treatment and reimbursement for neurovascular illnesses, particularly in emerging countries, are factors that will accelerate market expansion.

The use of artificial intelligence (AI) in neurology is anticipated to support market expansion. Computer-aided diagnosis (CAD) systems that employ AI and advancedsignal processing methods can assist physicians in analyzing and understanding physiological signals and images more accurately.

New Market Entrants Focused on Advancements in Neurological Medical Devices

A condition known as hydrocephalus causes an abnormal accumulation of intracranial fluid in the brain cavities. The implantation of a shunt to drain the surplus accumulation is one of the traditional treatments for chronic hydrocephalus. Data on intracranial pressure is continuously monitored and collected by newer systems, such asInternet of Things (IoT) sensors.

New companies offer neuromodulation solutions to help patients manage their discomfort from neurological illnesses. Aspiring market players are working on developments in neurological medical devices.

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Key Takeaways from Market Study

Key Segments in Neurology Devices Industry Research

Quick Buy: Neurology Devices Market Research Report

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Winning Strategy

Top companies are engaged in the introduction of magnetic stimulation technology as a result of innovationsin neurostimulation and neuromodulation techniques. Key market players are collaborating with research organizations to increase their presence and contribute to the creation of modern as well as safe processes.

For instance,

Competitive Landscape

Key producers of neurology devices are focusing on innovation by manufacturing and releasing new products as part of their strategy to maintain their position in the neurology devices market on a worldwide scale. To diversify their product offerings, top neurology medical device companies are concentrating on getting new devices approved.

For instance :

Key Companies Profiled

More Valuable Insights on Offer

Fact.MR, in its new offering, presents an unbiased analysis of the global neurology devices market, presenting historical demand data (2017-2021) and forecast statistics for the period of 2022-2027.

The study divulges essential insights on the market on the basis of product (cerebrospinal fluid management devices, interventional neurology devices, neurosurgery devices, neurostimulation devices) and end user (hospitals, ambulatory surgery centers, neurology clinics), across five major regions (North America, Europe, Asia Pacific, Latin America, and MEA).

Check out more related studies published by Fact.MR Research:

Neurointerventional Devices Market:The neurointerventional devices market was worthUS$ 2.3 Bnin 2020, and is predicted to expand2.1Xby the end of the decade.North America dominates the global market, global demand for neurointerventional treatment devices is predicted to increase at a robust CAGR of8%through 2031. The proliferationof technology in surgical procedures has completely changed the way diseases are dealt with and treated. The prevalence of brain tumors, aneurysms, and strokes majorly influence the neurointerventional treatment market.

Neurology Diagnostics Devices Market:At the end of 2021, the global neurology diagnostics devices market was valued atUS$ 8.24 billionand is anticipated to reachUS$ 21.1 billionby 2032, expanding at a highCAGR of 8.9%over the 2022-2032 time frame. As per a detailed industry analysis by Fact.MR, market research and competitive intelligence provider, CT Scanners dominated the global market in 2021 accounting for37.3%market share.

Nerve Ablation Devices Market:The global market for nerve ablation devices market is estimated to be a growing market, as the industry players are coming up with a new innovative versions of nerve ablation devices, to reduce the pain while inserting the microelectrodes and also looking for increase the treatment area which can be more accurate for targeted nerve for pain.The nerve ablation devices market is expected to witness significant growth over the forecast period due to the increasing number of patients suffering from pain.

ENT Medical Devices Market:The global ENT medical devices market is gaining traction and is likely to ascend at around 5.5% CAGR during the forecast period of 2021 to 2031. While the advanced healthcare sector and growing awareness about health have resulted in an increasing geriatric population, it has surged the demand for ENT medical devices, owing to the rise in ENT-related issues in elderly people.

Wireless Portable Medical Devices Market:Fact.MRs wireless portable medical devices industry analysis reveals that the global market was valued atUS$ 15 Bnin 2020, and is projected to topUS$ 33 Bnby 2031, expanding at a CAGR of11%.Demand for monitoring devices is projected to expand at a CAGR of11%reaching a valuation of aroundUS$ 16 Bn, with that for medical therapeutic devices also surging at11%.

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World to Benefit from Rapid Implementation of Artificial Intelligence in Neurology Devices: Fact.MR Study - GlobeNewswire

News Release

Tuesday, September 13, 2022

The National Institutes of Health will invest $130 million over four years, pending the availability of funds, to accelerate the widespread use of artificial intelligence (AI) by the biomedical and behavioral research communities. The NIH Common Funds Bridge to Artificial Intelligence (Bridge2AI) program is assembling team members from diverse disciplines and backgrounds to generate tools, resources, and richly detailed data that are responsive to AI approaches. At the same time, the program will ensure its tools and data do not perpetuate inequities or ethical problems that may occur during data collection and analysis. Through extensive collaboration across projects, Bridge2AI researchers will create guidance and standards for the development of ethically sourced, state-of-the-art, AI-ready data sets that have the potential to help solve some of the most pressing challenges in human health such as uncovering how genetic, behavioral, and environmental factors influence a persons physical condition throughout their life.

Generating high-quality ethically sourced data sets is crucial for enabling the use of next-generation AI technologies that transform how we do research, said Lawrence A. Tabak, D.D.S., Ph.D., Performing the Duties of the Director of NIH. The solutions to long-standing challenges in human health are at our fingertips, and now is the time to connect researchers and AI technologies to tackle our most difficult research questions and ultimately help improve human health.

AI is both a field of science and a set of technologies that enable computers to mimic how humans sense, learn, reason, and take action. Although AI is already used in biomedical research and healthcare, its widespread adoption has been limited in part due to challenges of applying AI technologies to diverse data types. This is because routinely collected biomedical and behavioral data sets are often insufficient, meaning they lack important contextual information about the data type, collection conditions, or other parameters. Without this information, AI technologies cannot accurately analyze and interpret data. AI technologies may also inadvertently incorporate bias or inequities unless careful attention is paid to the social and ethical contexts in which the data is collected. In order to harness the power of AI for biomedical discovery and accelerate its use, scientists first need well-described and ethically created data sets, standards, and best practices for generating biomedical and behavioral data that is ready for AI analyses.

As it creates tools and best practices for making data AI-ready, Bridge2AI will also produce a variety of diverse data types ready to be used by the research community for AI analyses. These types include voice and other data to help identify abnormal changes in the body. Researchers will also generate data that can be used to make new connections between complex genetic pathways and changes in cell shape or function to better understand how they work together to influence health. In addition, AI-ready data will be prepared to help improve decision making in critical care settings to speed recovery from acute illnesses and to help uncover the complex biological processes underlying an individuals recovery from illness.

The Bridge2AI program is committed to fostering the formation of research teams richly diverse in perspectives, backgrounds, and academic and technical disciplines. Diversity is fundamental to the ethical generation of data sets, and for training future AI technologies to reduce bias and improve effectiveness for all populations, including those who are underrepresented in biomedical and behavioral research. Bridge2AI will develop ethical practices for data generation and use, addressing key issues such as privacy, data trustworthiness, and reducing bias.

NIH has issued four awards for data generation projects, and three awards to create a Bridge Center for integration, dissemination and evaluation activities. The data generation projects will generate new biomedical and behavioral data sets ready to be used for developing AI technologies, along with creating data standards and tools for ensuring data are findable, accessible, interoperable, and reusable, a principle known as FAIR. In addition, data generation projects will develop training materials that promote a culture of diversity and the use of ethical practices throughout the data generation process. The Bridge Center will be responsible for integrating activities and knowledge across data generation projects, and disseminating products, best-practices, and training materials.

The Bridge2AI program is an NIH-wide effort managed collaboratively by the NIH Common Fund, the National Center for Complementary and Integrative Health, the National Eye Institute, the National Human Genome Research Institute, the National Institute of Biomedical Imaging and Bioengineering, and the National Library of Medicine. To learn more about the Bridge2AI program, visit the Musings from the Mezzanine blog from the National Library of Medicine, and watch this video about the Bridge2AI program.

About the NIH Common Fund: The NIH Common Fund encourages collaboration and supports a series of exceptionally high-impact, trans-NIH programs. Common Fund programs are managed by the Office of Strategic Coordination in the Division of Program Coordination, Planning, and Strategic Initiatives within the NIH Office of the Director in partnership with the NIH Institutes, Centers, and Offices. More information is available at the Common Fund website: https://commonfund.nih.gov.

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

NIHTurning Discovery Into Health

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NIH launches Bridge2AI program to expand the use of artificial intelligence in biomedical and behavioral research - National Institutes of Health...

Casey Greene, PhD, chair of the University of Colorado School of Medicines Department of Biomedical Informatics, is working toward a future of serendipity in healthcare using artificial intelligence (AI) to help doctors receive the right information at the right time to make the best decision for a patient.

Finding that serendipity begins with the data. Greene said the Departments faculty works with data ranging from genomic-sequencing information, cell imaging, and electronic health records. Each area has its own robust constraints ethical and privacy protections to ensure that the data are being used in accordance with peoples wishes.

His team uses petabytes of sequencing data that are available to anyone, Greene said. I think its empowering, he said, noting that anyone with an internet connection can conduct scientific research.

Following the selection or creation of a data set, Greene and other AI researchers at the CU Anschutz Medical Campus begin the core focus of AI work building algorithms and programs that can detect patterns. The goal is to find links in these large data sets that ultimately offer better treatments for patients. Still, human insight brings essential perspectives to the research, Greene said.

The algorithms do learn patterns, but they can be very different patterns and can become confused in interesting ways, he said. Greene used a hypothetical example of sheep and hillsides, two things often seen together. Researchers must teach the program to separate the two items, he said.

A person can look at a hillside and see sheep and recognize sheep. They can also see a sheep somewhere unexpected and realize that the sheep is out of place. But these algorithms don't necessarily distinguish between sheep and hillsides at first because people usually take pictures of sheep on hillsides. They don't often take pictures of sheep at the grocery store, so these algorithms can start to predict that all hillsides have sheep, Greene said.

It's a little bit esoteric when you're thinking about hillsides and sheep, he said. But it matters a lot more if you're having algorithms that look at medical images where you'd like to predict in the same way that a human would predict based on the content of the image and not based on the surroundings. Encoding prior human knowledge (knowledge engineering) into these systems can lead to better healthcare down the line, Greene said.

And when it comes to AI in healthcare, Greene said it is key to have open models and diverse teams doing the work. It gives others a chance to probe these models with their own questions. And I think that leads to more trust.

In the Q&A below, Greene provides a general overview of the terms and technology behind AI alongside the challenges he and his fellow researchers face.

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What Is Artificial Intelligence in Healthcare? - University of Colorado Anschutz Medical Campus

As Artificial Intelligence is applied to car audio, the system can start to sense competing noise ... [+] and adjust the experience dynamically.

Hollywood has perennially portrayed Artificial Intelligence (AI) as the operating layer of dystopian robots who replace unsuspecting humans and create the escalating, central conflict. In a best case reference, you might imagine a young Hailey Joel Osment playing David, the self-aware, artificial kid in Spielbergs polar-caps-thawed-and-flooded-coastal-cities world (sound familiar?) of AI: Artificial Intelligence who (spoiler alert) only kills himself. Or maybe you recall Robin Williamss voice as Bicentennial Man who, once again, is a self-aware robot attempting to thrive who (once again on the spoiler alert), ends up being his only victim. And, of course, theres the nearly clich reference to Terminator and its post-apocalyptic world with machines attempting to destroy humans and, well, (not-so-spoiler alert) lots of victims over a couple of decades. In none of these scenarios, however, do humans coexist with an improved life, let alone enhanced entertainment and safety.

That, however, is the new reality. Artificial Intelligence algorithms can be included into audio designs and continuously improved via over-the-air updates to improve the driving experience. And in direct contradiction to these Hollywood examples, such AI might actually improve the humans likelihood to survive.

How the car audio performs can now become an innovative, self-tuned system that enhances the ... [+] experience for the user.

Until recently, all User Interface (UI) including audio development has required complex programming by expert coders over the standard thirty-six (36) months of a vehicle program. Sheet metal styling and electronic boxes are specified, sourced and developed in parallel only to calibrate individual elements late in development. Branded sounds. Acoustic signatures. All separate initiatives within the same, anemic system design that has cost manufacturers billions.

But Artificial Intelligence has allowed a far more flexible and efficient way of approaching audio experience design. What were seeing is the convergence of trends, states Josh Morris, DSP Concepts Machine Learning Engineering Manager. Audio is becoming a more dominant feature within automotive, but at the same time youre seeing modern processors become stronger with more memory and capabilities.

And, therein, using a systems-focused development platform, Artificial Intelligence and these stronger processors provides drivers and passengers with a new level of adaptive, real-time responsiveness. . Instead of the historical need to write reams of code for every conceivable scenario, AI guides system responsiveness based on a learned awareness of environmental conditions and events, states Steve Ernst, DSP Concepts Head of Automotive Business Development.

The very obvious way to use such a learning system is de-noising the vehicle so that premium audio can be tailored and improved despite having swapped to winter tires or other such ambient changes. But LG Electronics has developed algorithms running in the DSP Concepts Audio Weaver platform to allow voice enhancements of the movies dialogue during rear-seat entertainment to accentuate it versus in-movie explosions, thereby allowing the passenger to better hear the critical content

Another non-obvious aspect would be how branded audio sounds are orchestrated in the midst of other noises. Does this specific vehicle require the escalating boot-up sequence to play while other sounds like the radio and chimes are automatically turned down? Each experience can be adjusted.

How to deal with the ongoing, internal, external and ever-changing audio alerts will be a ... [+] development challenge for autonomous and electric vehicles alike.

As the world races into both electric vehicles and autonomous driving, the frequency and needs of audible warnings will likely change drastically. For instance, an autonomous taxis safety engineer cannot assume the passengers are anywhere near a visual display when a timely alert is required. And how audible is that alert for the nearly 25 million Americans with disabilities for whom autonomous vehicles should open new mobility possibilities? Audio now isnt just for listening to your favorite song, states Ernst. With autonomous driving, there are all sorts of alerts that are required to keep the driver engaged or to alert the non-engaged driver about things going on around them.

And what makes it more challenging, injects Adam Levenson, DSP Conceptss Head of Marketing, are all of the things being handled simultaneously within the car: telephony, immersive or spatial sound, engine noise, road noise, acoustic vehicle alert systems, voice systems, etc. We like to say the most complex audio product is the car.

For instance, imagine the scenario where a driver has enabled autonomous drive mode on the highway, has turned up his tunes and is pleasantly ignorant of an approaching emergency vehicle. At what accuracy (and distance) of siren-detection using the vehicles microphone(s) does the car alert its quasi-distracted-driver? How must that alert be presented to overcome ambient noise, provide sufficient attention but not needlessly startle the driver? All of this can be tuned via pre-developed models, upfront training with different sirens and subsequent cloud-based tuning. This is where the overall orchestration becomes really important, explains Morris. We can take the output of the [AIs detection] model and direct that to different places in the car. Maybe you turn the audio down, trigger some audible warning signal and flash something on the dashboard for the driver to pay attention.

The same holds true for external alerts. For instance, quiet electric vehicle may have tuned alarms for pedestrians. And so new calibrations can be created offline and downloaded to vehicles as software updates based upon the enabled innovation.

Innovation everywhere. And Artificial Intelligence feeding the utopian experience rather than creating Hollywoods dystopian world.

Heres my prediction of the week (and its only Tuesday, folks): the next evolution of audio shall include a full, instantaneous feedback loop including the subtle, real-time users delight. Yes, much of the current design likely improves the experience, but an ongoing calibration of User-Centered Design (UCD) might be additionally enhanced based upon the passengers expressions, body language and comments, thereby individually tuning the satisfaction in real-time. All of the enablers are all there: camera, AI, processors and an adaptive platform.

Yes, weve previously heard of adaptive mood lighting and remote detection of boredom, stress, etc. to improve safety, but nothing that enhances the combined experience based upon real-time, learning algorithms of all user-pointed sensors.

Maybe Im extrapolating too much. But just like Robin Williamss character Ive spanned two centuries so maybe Im also just sensitive to what humans might want.

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The New Artificial Intelligence Of Car Audio Might Improve More Than Just Tunes - Forbes