Category Archives: Artificial Intelligence

Xiaomi has unveiled MIUI 13 which it plans to unleash on the world in the first quarter of the new year. The firm said that the operating system will be expanded beyond smartphones and tablets to Artificial Intelligence of Things (AIoT) devices such as smart watches, speakers, and TVs. The firm has also improved its software so that it operates better under heavy usage.

According to the company, MIUI 13 improves core functions, increasing the systems fluidity by a whopping 52%. The core apps have also been optimised so they run better while the system is getting bogged down by third-party apps. Xiaomi has also developed technologies called Atomized Memory and Liquid Storage which reduce deterioration by over 5% over a 36-month period; this should help you hold onto devices for longer.

To make MIUI more interoperable with smart devices, the new update will introduce the beta of Mi Smart Hub. Commenting on the new tool, Xiaomi said:

As of Q3 2021, the number of connected devices on Xiaomis IoT platform exceeds 400 million. While leading the industry with its smart hardware portfolio, MIUI 13 will introduce the beta of Mi Smart Hub, which will help realize a more connected experience between smart devices. With Mi Smart Hub, users can find nearby devices and with a simple gesture to seamlessly share and access content such as music, display, even apps across multiple devices.

Finally, MIUI 13 brings new personalisation options through new widgets, dynamic wallpapers, and more. The global version of MIUI 13 will be delivered over-the-air beginning in Q1 2022. The first devices to get the update will be the Mi 11, Mi 11 Ultra, Mi 11i, Mi 11X Pro, Mi 11X, Xiaomi Pad 5, Redmi 10, Redmi 10 Prime, Xiaomi 11 Lite 5G NE, Xiaomi 11 Lite NE, Redmi Note 8 (2021), Xiaomi 11T Pro, Xiaomi 11T, Redmi Note 10 Pro, Redmi Note 10 Pro Max, Redmi Note 10, Mi 11 Lite 5G, Mi 11 Lite, and Redmi Note 10 JE.

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Xiaomi debuts MIUI 13 with support for the Artificial Intelligence of Things - Neowin

DUBLIN, Dec. 22, 2021 /PRNewswire/ -- The "Artificial Intelligence In Remote Patient Monitoring Market Research Report by Product, by Solution, by Technology, by Application, by Region - Global Forecast to 2026 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence In Remote Patient Monitoring Market size was estimated at USD 712.67 million in 2020 and expected to reach USD 892.99 million in 2021, at a CAGR 25.63% to reach USD 2,803.19 million by 2026.

Market Statistics:

The report provides market sizing and forecast across five major currencies - USD, EUR GBP, JPY, and AUD. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2019 are considered historical years, 2020 as the base year, 2021 as the estimated year, and years from 2022 to 2026 are considered the forecast period.

Competitive Strategic Window:

The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence In Remote Patient Monitoring Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

Company Usability Profiles:

The report profoundly explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence In Remote Patient Monitoring Market, including 100 Plus, AiCure, Binah.ai, Biofourmis, Cardiomo, ChroniSense Medical, ContinUse Biometrics (Cu-Bx), Current Health, Ejenta, Eko, Engagely.ai, Feebris, GYANT, iHealth, Medical Device + Diagnostic Industry (MD+DI), Medopad, Myia, Neoteryx, LLC, Neteera, Tech Vedika, ten3T Healthcare, and Vitls.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on the market offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:1. What is the market size and forecast of the Global Artificial Intelligence In Remote Patient Monitoring Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Artificial Intelligence In Remote Patient Monitoring Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Artificial Intelligence In Remote Patient Monitoring Market?4. What is the competitive strategic window for opportunities in the Global Artificial Intelligence In Remote Patient Monitoring Market?5. What are the technology trends and regulatory frameworks in the Global Artificial Intelligence In Remote Patient Monitoring Market?6. What is the market share of the leading vendors in the Global Artificial Intelligence In Remote Patient Monitoring Market?7. What modes and strategic moves are considered suitable for entering the Global Artificial Intelligence In Remote Patient Monitoring Market?

Key Topics Covered:

1. Preface

2. Research Methodology

3. Executive Summary

4. Market Overview4.1. Introduction4.2. Cumulative Impact of COVID-19

5. Market Dynamics5.1. Introduction5.2. Drivers5.2.1. ICT infrastructure development in developing countries5.2.2. Rise in adoption of AI in remote patient monitoring due to real time monitoring and improved patient engagement5.2.3. Growth in demand due to optimizing management and lower human errors5.3. Restraints5.3.1. Lack of awareness in remote areas5.3.2. Expensive as compared to traditional facilities5.4. Opportunities5.4.1. Rapid digitalization and extensive use of social media of consumer5.4.2. Shift in trend towards wearable technology5.5. Challenges5.5.1. Increasing concern related to cybersecurity and privacy

6. Artificial Intelligence In Remote Patient Monitoring Market, by Product6.1. Introduction6.2. Special Monitors6.2.1. Anaesthesia Monitors6.2.2. Blood Glucose Monitor6.2.3. Cardiac Rhythm Monitor6.2.4. Fetal Heart Rate Monitor6.2.5. Multi-Parameter Monitors6.2.6. Prothrombin Monitors6.2.7. Respiratory Monitor6.3. Vital Monitors6.3.1. Blood Pressure Monitor6.3.2. Brain Monitor6.3.3. Heart Rate Monitor6.3.4. Pulse Oximeter6.3.5. Respiratory Monitor6.3.6. Temperature Monitor

7. Artificial Intelligence In Remote Patient Monitoring Market, by Solution7.1. Introduction7.2. Hardware7.3. Services7.4. Software

8. Artificial Intelligence In Remote Patient Monitoring Market, by Technology8.1. Introduction8.2. Machine Learning (ML)8.3. Natural Language Processing (NLP)8.4. Querying Method (QM)8.5. Speech Recognition (SR)

9. Artificial Intelligence In Remote Patient Monitoring Market, by Application9.1. Introduction9.2. Cancer9.3. Cardiovascular Diseases9.4. Dehydration9.5. Diabetes9.6. Infections9.7. Respiratory Issues9.8. Sleep Disorder9.9. Viral Infection9.10. Weight Management & Fitness Monitoring

10. Americas Artificial Intelligence In Remote Patient Monitoring Market10.1. Introduction10.2. Argentina10.3. Brazil10.4. Canada10.5. Mexico10.6. United States

11. Asia-Pacific Artificial Intelligence In Remote Patient Monitoring Market11.1. Introduction11.2. Australia11.3. China11.4. India11.5. Indonesia11.6. Japan11.7. Malaysia11.8. Philippines11.9. Singapore11.10. South Korea11.11. Taiwan11.12. Thailand

12. Europe, Middle East & Africa Artificial Intelligence In Remote Patient Monitoring Market12.1. Introduction12.2. France12.3. Germany12.4. Italy12.5. Netherlands12.6. Qatar12.7. Russia12.8. Saudi Arabia12.9. South Africa12.10. Spain12.11. United Arab Emirates12.12. United Kingdom

13. Competitive Landscape13.1. FPNV Positioning Matrix13.1.1. Quadrants13.1.2. Business Strategy13.1.3. Product Satisfaction13.2. Market Ranking Analysis13.3. Market Share Analysis, By Key Player13.4. Competitive Scenario13.4.1. Merger & Acquisition13.4.2. Agreement, Collaboration, & Partnership13.4.3. New Product Launch & Enhancement13.4.4. Investment & Funding13.4.5. Award, Recognition, & Expansion

14. Company Usability Profiles14.1. 100 Plus14.2. AiCure14.3. Binah.ai14.4. Biofourmis14.5. Cardiomo14.6. ChroniSense Medical14.7. ContinUse Biometrics (Cu-Bx)14.8. Current Health14.9. Ejenta14.10. Eko14.11. Engagely.ai14.12. Feebris14.13. GYANT14.14. iHealth14.15. Medical Device + Diagnostic Industry (MD+DI)14.16. Medopad14.17. Myia14.18. Neoteryx, LLC14.19. Neteera14.20. Tech Vedika14.21. ten3T Healthcare14.22. Vitls

15. Appendix

For more information about this report visit https://www.researchandmarkets.com/r/7x6wlp

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

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Insights on the Artificial Intelligence in Remote Patient Monitoring Global Market to 2026 - Featuring 100 Plus, AiCure and Cardiomo Among Others -...

Dublin, Dec. 17, 2021 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence Growth Opportunities" report has been added to ResearchAndMarkets.com's offering.

As artificial intelligence (AI) and machine learning (ML) will transform businesses, it will create a broad spectrum of new revenue opportunities for ICT vendors and service providers.

The opportunities cut across advisory services, applications, and infrastructure. As the mega trends shape the AI landscape, it will have a ripple effect in terms of new revenue and growth opportunities for start-ups as well as large global information and communication technology (ICT) companies.

Artificial intelligence leverages algorithms and large datasets to identify underlying relationships and drive new or better business outcomes. While still at a nascent stage, AI technologies are being adopted across industries globally to innovate business models, drive operational efficiencies, and create strategic differentiation.

The potential impacts of AI on people, organizations, and society are widespread. COVID-19 pandemic further accelerated the pace of digital transformation and AI adoption as organizations seek to explore new means of creating sustainable business models as well as drive customer value, effectively manage the employee lifecycle in a distributed environment, and optimize costs.

The AI ecosystem is evolving rapidly making it essential to understand the overarching trends that are impacting AI and its adoption.

Further, as we expect democratizing of AI/ML, there will be a move slowly away from applications that only can be developed by data scientists for platforms, making it easier to develop and deploy solutions.

Some of these trends include:

Augmenting AI capabilities with enterprise applications

Advancements in cognitive capabilities to assess emotions and sentiments

Adoption of Edge AI

Public cloud service providers playing a pivotal role in the AI ecosystem

Focus on ethical AI

Key Topics Covered:

Story continues

1. Strategic Imperatives

Why is it Increasingly Difficult to Grow?

The Strategic Imperative

The Impact of the Top Three Strategic Imperatives on Artificial Intelligence

Growth Opportunities Fuel the Growth Pipeline Engine

2. Growth Environment

3. Growth Opportunity Analysis

Key Trends in the Artificial Intelligence Industry

Augmenting AI Capabilities with Enterprise Applications

Advancements in Cognitive Capabilities to Assess Emotions and Sentiments - Emotion Artificial Intelligence

Adoption of Edge AI

Public Cloud Service Providers are Playing a Pivotal Role in the AI Ecosystem

Focus on Ethical AI

4. Way Forward

5. Growth Opportunity Universe - Artificial Intelligence

Growth Opportunity 1: Consulting and Advisory Services for AI Roadmap

Growth Opportunity 2: Industry Vertical/Function-specific Applications to Enhance Customer Value

Growth Opportunity 3: Edge Data Centers for Supporting Select AI Use Cases

Growth Opportunity 4: Integration Services to Build Customized Solutions for AI by Leveraging Emerging Technologies

For more information about this report visit https://www.researchandmarkets.com/r/haruam

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Global Artificial Intelligence Market Opportunities Report 2021 with Focus on Transformative Mega Trends of AI - Yahoo Finance

Artificial intelligence, or AI, support systems, automation and robotics have the potential to transform agriculture. A group of Southeastern universities is coordinating a conference to address this potential at Auburn University in the spring.

Titled Envisioning 2050 in the Southeast: AI-driven Innovations in Agriculture, the conference will be held March 9-11 at The Hotel at Auburn University and Dixon Conference Center. It is a joint effort from all the land-grant universities in the Southeast with funding from the USDA National Institute of Food and Agriculture.

"This conference is multipurpose, said Brenda Ortiz, co-organizer of the conference and professor and extension specialist in the Department of Crop, Soil and Environmental Sciences at Auburn. The primary purpose is to share knowledge, expertise and resources among Southeastern universities, stakeholders and industries on artificial intelligence-driven innovations that can be applied to agriculture.

Ortiz said the aim is also to provide a space where university faculty, industry members and stakeholders can discuss how artificial intelligence innovations, automation and robotics could strengthen agricultural production, bolster environmental sustainability and increase resiliency.

Invited speakers from academia, the federal government and the industry itself will share their work in areas such as crop production, plant and animal breeding, climate, agricultural extension, pedagogy, food processing and supply chain, livestock management and many more.

This two-and-a-half-day conference will include a combination of invited plenary presentations, two panel sessions and breakout sessions that include invited oral presentations, facilitated working sessions, a poster session and a pre-conference workshop on current and advanced AI-driven data analysis.

Kati Migliaccio, co-organizer of the conference and professor and chair of the Department of Agricultural and Biological Engineering at the University of Florida, said the timing of the conference is perfect.

This is an opportune time to host this conference focusing on AI in agriculture in the Southeast because of the resources invested in AI, the state of innovation of AI in agriculture and the critical need to adapt agriculture for current world challenges, including labor, nutrition, energy and climate, she said.

It brings together expertise from all sectorsindustry, academics, government and stakeholdersto focus on agriculture innovation with AI particularly for the Southeast. The format of this conference was created to allow for knowledge sharing, as well as networking and greater exploration for future endeavors. Attending this event will provide the spark for further innovation and collaboration amongst those with AI and agricultural interests across professions and across disciplines.

University faculty and students, industry members and representatives of different stakeholder groups will share expertise, vision and needs for the development of AI-driven agriculture in the Southeast. Participants will also learn about the development of AI in agriculture at various universities and the growth of resources, infrastructure and personnel available for AI and related technology in agriculture and natural resources.

Ortiz said in-person participation will allow networking, discussions for future collaboration and first-hand knowledge exchange. Registration is open and limited based on space availability. For those who cannot attend in person, a limited, virtual option will be available that will include access to plenary and breakout sessions. More details and registration information are available on the conference website.

Graduate students and postdoctoral scientists are also welcome, Ortiz said, and invited to submit abstracts to the poster presentation. Submissions must be made online by Jan. 17.

Ortiz said she would encourage her peers to attend to better learn advances in artificial intelligence-driven applications in agriculture not only across the Southeast, but across the nation, and to explore opportunities for collaboration among others breaking this same ground.

This is an opportunity to be part of a regional effort to outline and design the future of agriculture and discuss the role of technology and innovation, Ortiz said. Artificial intelligence is gaining traction and momentum in agriculture, but there is potential still to be tapped.

(Written by Kristen Bowman)

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Auburn University hosting conference to address artificial intelligence in agriculture - Office of Communications and Marketing

Written By ESR News Blog Editor Thomas Ahearn

In November 2021, the New York City (NYC) Council passed a measure Int. No. 1894-A that will require employers that use Artificial Intelligence (AI) in the form of an automated employment decision tool to promote or screen job candidates to undergo a bias audit every year. The local law will take effect on January 2, 2023.

The bill would also require candidates or employees to be notified about the use of automated employment decision tools for hire or promotion, and about the job qualifications and characteristics used by the tool. Violators would be subject to civil penalties of $500 for first-time violations and up to $1,500 for repeat offenses.

An automated employment decision tool is a tool that automates, supports, substantially assists, or replaces discretionary decision-making processes and materially impacts natural persons, such as a junk email filter, firewall, antivirus software, calculator, spreadsheet, database, data set, or other compilation of data.

The bias audit would be an impartial evaluation by an independent auditor that would include but not be limited to the testing of an automated employment decision tool to assess the tools disparate impact on protected persons and to test whether the AI tool discriminates on the basis of race, sex, or other protected categories.

In October 2021, the U.S. Equal Employment Opportunity Commission (EEOC) a government agency that enforces federal laws prohibiting employment discrimination launched an initiative to ensure that AI tools used in hiring and other employment decisions comply with federal civil rights laws that the agency enforces.

Artificial intelligence and algorithmic decision-making tools have great potential to improve our lives, including in the area of employment. At the same time, the EEOC is keenly aware that these tools may mask and perpetuate bias or create new discriminatory barriers to jobs, EEOC Chair Charlotte A. Burrows stated in a press release.

Employment Screening Resources (ESR) a service offering of ClearStar, a leading provider of Human Capital Integrity technology-based services offers background screening services to help employers to make informed hiring decisions on job candidates. To learn more about background screening, contact ESR today.

NOTE: Employment Screening Resources (ESR) a service offering of ClearStar does not provide or offer legal services or legal advice of any kind or nature. Any information on this website is for educational purposes only.

2021 Employment Screening Resources (ESR) A Service Offering of ClearStar Making copies of or using any part of the ESR News Blog or ESR website for any purpose other than your own personal use is prohibited unless written authorization is first obtained from ESR.

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NYC to Audit Employers Using Artificial Intelligence to Screen Job Candidates - ESR NEWS

Artificial Intelligence (AI) can predict with up to 92% accuracy if a person will develop dementia within two years and can also cut the number of people wrongly diagnosed, according to a new study.

The AI technique called machine learning works by spotting hidden patterns in data and learning who is most at risk.

Dementia is a syndrome (a group of related symptoms) associated with an ongoing decline of brain functioning.

Symptoms may include problems with memory loss, thinking speed, mental sharpness and quickness, language, such as using words incorrectly, or trouble speaking, understanding, judgement, mood, movement, and difficulties doing daily activities.

Researchers from the University of Exeter looked at data over a 10-year period from 15,300 patients who attended a network of 30 memory clinics in the US.

The attendees did not have dementia at the start of the study, though many were experiencing problems with memory or other brain functions.

Between 2005 and 2015, one in 10 attendees (1,568) received a new diagnosis of dementia within two years of their visit.

The machine learning algorithm predicted new dementia cases with up to 92% accuracy, significantly more precise than two existing alternative research methods, the team found.

Dementia misdiagnoses

The researchers also found for the first time that around 8% (130) of the dementia diagnoses appeared to be made in error, as the diagnosis was later reversed.

Machine-learning models accurately identified more than 80% of these inconsistent diagnoses, according to the study, published in JAMA Network Open.

AI can not only accurately predict who will be diagnosed with dementia, but has the potential to improve the accuracy of the diagnoses, the research suggests.

There are over 55 million people worldwide living with dementia in 2020, according to Alzheimer's Disease International.

And the organisation believes this number will almost double every 20 years, reaching 78 million in 2030 and 139 million in 2050.

Machine learning uses patient information routinely available in the clinics, such as memory and brain function, performance on cognitive tests and specific lifestyle factors.

Professor David Llewellyn, an Alan Turing fellow based at the University of Exeter, said: "We're now able to teach computers to accurately predict who will go on to develop dementia within two years.

"We're also excited to learn that our machine-learning approach was able to identify patients who may have been misdiagnosed.

"This has the potential to reduce the guesswork in clinical practice and significantly improve the diagnostic pathway, helping families access the support they need as swiftly and as accurately as possible."

The future of diagnosing dementia

Researchers hope to continue their research to understand the practical use of AI technology within clinics.

"Artificial intelligence has huge potential for improving early detection of the diseases," said Dr Rosa Sancho, head of research at Alzheimer's Research UK.

"[This] could revolutionise the diagnosis process for people concerned about themselves or a loved one showing symptoms," she added.

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Artificial Intelligence: AI technology can accurately predict who will develop dementia within two years, says study - Sky News

The tactical advantages of AI-enabled warfare and weaponry may seem far too numerous to cite, yet the majority of them pertain to one clear, simple conceptspeed.

The speed of decision-making, when mere seconds can decide life or death in warfare, is being completely redefined through the advent of artificial intelligence (AI). AI-empowered computers can take pools of incoming data from otherwise disparate sensor streams of information, organize andperform analyticson the information,and use it to solve problems, make determinations and recommend courses of action.

We're trying to reduce the decision time and we're trying to reduce the cognitive burden on the commander on the battlefield. If you look into the future, the battlefield will be more expansive. Decisions will be required more rapidly, Maj. Gen. Ross Coffman, Director forNext-Generation Combat Vehicles Cross-Functional Team at the Army Futures Command, told the National Interest in an interview.

AI-capable algorithms are only as effective as the scope of the database they draw from allows, so much of the cataloged information can at times pertain to previous instances of elements ofhistory relevantto the current matters being analyzed.

The concept is to utilize the attributes and faculties unique to human cognition in the most optimal way by leveraging high-speed analytics and AI-capable computing to perform otherwise time-consuming procedural tasks. The intended effect is often described as easing the cognitive burden to better empower battlefield commanders with an ability to make decisions on an exponentially faster timetable.

We're leveraging artificial intelligence. We're leveraging computer-generated machine learning to create decision space for commanders on the battlefield. There's no doubt in our mind who the customer is. The customer is the commander of the future, Coffman said.

Various applicationsof AI and Machine Learning were explored recently at a Northrop Grumman sponsored live-fire event in Arizona as a way to expedite the targeting process and greatly decrease thetime neededto find enemy targets, identify them and quickly decide which weapon or effector is most optimal for destroying the target.

We use that to help the gunners identify ground and air targets because as you know, at the extended ranges, the human eye cannot see the target or identify it. So we use machine learning to amplify the image,Rob Menti, Business Development Director atNorthrop Grumman, told the National Interest during a Bushmaster Users Conference Demonstration this past October demo in Arizona.

Kris Osborn is the defense editor for theNational Interest. Osborn previously served at the Pentagon as a Highly Qualified Expert with the Office of the Assistant Secretary of the ArmyAcquisition, Logistics & Technology. Osborn has also worked as an anchor and on-air military specialist at national TV networks. He has appeared as a guest military expert on Fox News, MSNBC, The Military Channel, and The History Channel. He also has a Masters Degree in Comparative Literature from Columbia University.

Image: DVIDS

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The Speed of Warfare Is Getting FasterThanks to Artificial Intelligence - The National Interest

DUBLIN, December 15, 2021--(BUSINESS WIRE)--The "Healthcare Artificial Intelligence Market - Forecasts from 2021 to 2026" report has been added to ResearchAndMarkets.com's offering.

The healthcare artificial intelligence market is projected to grow at a CAGR of 39.97% to reach US$36.222 billion by 2026 from US$3.441 billion in 2019.

Artificial Intelligence essentially uses machine learning algorithms and deep learning to gather and process data and furnish it to the end-user. The foremost aim of using healthcare artificial intelligence is to scrutinize relationships between prevention techniques and patient results. It is thus used to analyze a chunk of data through Electronic Health Records to prevent disease.

A major reason for the growth of this market is the increase in the number of chronic diseases and fewer health care facilities available.

According to the World Economic Forum report, "One in three adults worldwide has multiple chronic conditions: cardiovascular disease alongside diabetes, depression as well as cancer, or a combination of three, four, or even five or six diseases at the same time. NCDs represent more than half the global burden of diseases.

With the spread of such chronic diseases, globally, the health care industry has recognized the importance of healthcare artificial intelligence. Artificial Intelligence will help to monitor and diagnose the patient status efficiently and effectively and will also enable efficient follow-ups. The technological advances and funding by both the private and public sectors are expected to drive the demand for this market in the forecast period.

There have been numerous technological advances in the field of Artificial Intelligence, globally. Many pharmaceutical companies are constantly working on up-gradation. Many health care artificial intelligence startups are encouraged across the world. Talking about Asia, in China, many startups are benefited from the government's strategic development plans.

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The Chinese government is constantly promoting private-public partnerships. Recently, a Chinese artificial intelligence health care startup synyi raised US $ 36.3 million. Similarly, icarbonX received funding of US$ 200 million from various investors to expand its scope of advanced artificial intelligence to cure diseases.

The government of India is also funding the various AI programs and has also collaborated with the Ministry of Electronics and Information Technology (MeitY), the National E-Governance Division (NeGD), and the National Association of Software and Service Companies (NASSCOM) to build the AI healthcare future. North American countries have also invested a lot of funds into the healthcare AI market.

The global technology revolution is at pace, Electronic health record machines are enhancing, the global health care AI market is expected to flourish.

Artificial Intelligence Health care is expected to add value in various administrative and operational clinics. It is also expected to promote social distancing by reducing human contact and protecting public and health care staff by minimizing the time spent on claim processing.

Due to the surge of COVID-19, many AI-POWERED cameras are deployed in Singapore to reduce the need for the workforce required to check the one-to-one temperature. COVID-19 has surely moved people to focus on their personal health and adopt technologically driven health care methods.

Key Topics Covered:

1. Introduction

1.1. Market Definition

1.2. Market Segmentation

2. Research Methodology

2.1. Research Data

2.2. Assumptions

3. Executive Summary

3.1. Research Highlights

4. Market Dynamics

4.1. Market Drivers

4.2. Market Restraints

4.3. Porters Five Forces Analysis

4.4. Industry Value Chain Analysis

5. Healthcare Artificial Intelligence Market Analysis, by Offering

5.1. Introduction

5.2. Hardware

5.3. Software

5.4. Services

6. Healthcare Artificial Intelligence Market Analysis, by Application

6.1. Introduction

6.2. Medical Imaging and Diagnostics

6.3. Precision Medicine

6.4. Lifestyle Management and Monitoring

6.5. Virtual Assistant

6.6. Wearables

6.7. Inpatient Care and Hospital Management

6.8. Drug Discovery and Development

6.9. Research

7. Healthcare Artificial Intelligence Market Analysis, by Geography

7.1. Introduction

8. Competitive Environment and Analysis

8.1. Major Players and Strategy Analysis

8.2. Emerging Players and Market Lucrativeness

8.3. Mergers, Acquisitions, Agreements, and Collaborations

8.4. Vendor Competitiveness Matrix

9. Company Profiles

Caption Health, Inc.

Intel Corporation

NVIDIA Corporation

Google

IBM Watson Health

Enlitic, Inc.

Lumiata

AiCure, LLC

Butterfly Network, Inc

ICarbon X

For more information about this report visit https://www.researchandmarkets.com/r/psjhx4

View source version on businesswire.com: https://www.businesswire.com/news/home/20211215005859/en/

Contacts

ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.com

For E.S.T Office Hours Call 1-917-300-0470For U.S./CAN Toll Free Call 1-800-526-8630For GMT Office Hours Call +353-1-416-8900

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$36.22 Billion Healthcare Artificial Intelligence Markets - Global Forecasts from 2021 to 2026 - ResearchAndMarkets.com - Yahoo Finance

LONDON--(BUSINESS WIRE)--Deep Knowledge Analytics, Big Innovation Centre and Innovation Eye launch 'Artificial Intelligence in the UK: Industry landscape overview in 2021', the most comprehensive industry mapping made to date, profiling, categorising and analysing over 3,600 private and public sector entities across 20 sectors and 50 locations in the UK.

The open-access report and IT platform, covers the latest developments in technology and innovation, best ranking companies and investors hubs, AI and COVID-19, policy and ethics, AI challenges and opportunities for the UK. Additionally, it profiles the top 100 UK AI experts and hubs, including think tanks, tech-hubs and doctoral training centres.

The UK remains first in Europe and ranks third globally, behind the USA and China in developing AI technologies with a reported 9 billion investment growth for the industry between 2019 and 2021. The UK government continues to foster growth and industry development through initiatives in 5 key areas - human capital, lab to market developments, networking, regulation and infrastructure.

Backed by the All-Party Parliamentary Group on Artificial Intelligence (APPG AI), the report ranks London as the most attractive city for investment and talent with over 65% of the UK's AI companies and start-ups headquartered in the capital. London also remains Europe's first in investment by sector with finance and marketing and advertisement as the highest funded. Cambridge and Edinburgh ranked second and third on the list of AIs tech hubs.

London accommodates 1300 AI companies, comprising 65% of the entire UK AI industry ecosystem. More broadly, it is notable that 700 other AI companies are distributed across the UK, making London the AI capital of Europe and the UK a true AI nation.' said Dmitry Kaminskiy, Founder of Deep Knowledge Analytics and Co-founder of Innovation Eye.

The 2021 analysis and report profile more than 2000 AI-centric companies across 20 AI sectors and 50 cities in the UK. In particular, the 2021 UK AI landscape overview breaks down, on a company-by-company basis, more than 13 billion worth of investments from 1500 investors into these AI companies.

The release also features several interactive mind maps by industries, technologies and influencers, and data visualisation to enable the analysis of the project's key findings in a more precise, dynamic and personalised way.

The report updates Innovation Eye's 'Artificial Intelligence Industry in the UK Landscape Overview', first produced by APPG AI in 2018.

Excerpt from:

The UK Leads Europe and Ranks Third Globally in Artificial Intelligence - Business Wire

Given the growing ubiquity of machine learning and artificial intelligence in healthcare settings, it's become increasingly important to meet patient needs and engage users.

And as panelists noted during a HIMSS Machine Learning and AI for Healthcare Forum session this week, designing technology with the user in mind is a vital way to ensure tools become an integral part of workflow.

"Big Tech has stumbled somewhat" in this regard, said Bill Fox, healthcare and life sciences lead at SambaNova Systems. "The patients, the providers they don't really care that much about the technology, how cool it is, what it can do from a technological standpoint.

"It really has to work for them," Fox added.

Jai Nahar, a pediatric cardiologist at Children's National Hospital, agreed, stressing the importance of human-centered AI design in healthcare delivery.

"Whenever we're trying to roll out a productive solution that incorporates AI," he said, "right from the designing [stage] of the product or service itself, the patients should be involved."

That inclusion should also expand to provider users too, he said: "Before rolling out any product or service, we should involve physicians or clinicians who are going to use the technology."

The panel, moderated by Rebekah Angove, vice president of evaluation and patient experience at the Patient Advocate Foundation, noted that AI is already affecting patients both directly and indirectly.

In ideal scenarios, for example, it's empowering doctors to spend more time with individuals. "There's going tobe a human in the loop for a very long time," said Fox.

"We can power the clinician with better information from a much larger data set," he continued. AI is also enabling screening tools and patient access, said the experts.

"There are many things that work in the background that impact [patient] lives and experience already," said Piyush Mathur, staff anesthesiologist and critical care physician at the Cleveland Clinic.

At the same time, the panel pointed to the role clinicians can play in building patient trust around artificial intelligence and machine learning technology.

Nahar said that as a provider, he considers several questions when using an AI-powered tool for his patient. "Is the technology really needed for this patient to solve this problem?" he said he asks himself. "How will it improve the care that I deliver to the patient? Is it something reliable?"

"Those are the points, as a physician, I would like to know," he said.

Mathur also raised the issue of educating clinicians about AI. "We have to understand it a little bit better to be able to translate that science to the patients in their own language," he said. "We have to be the guardians of making sure that we're providing the right data for the patient."

The panelists discussed the problem of bias, about which patients may have concerns and rightly so.

"There are multiple entry points at which bias can be introduced," said Nahar.

During the design process, he said, multiple stakeholders need to be involved to closely consider where bias could be coming from and how it can be mitigated.

As panelists have pointed out at other sessions, he also emphasized the importance of evaluating tools in an ongoing process.

Developers and users should be asking themselves, "How can we improve and make it better?" he said.

Overall, said Nahar, best practices and guidances need to be established to better implement and operationalize AI from the patient perspective and provider perspective.

The onus is "upon us to make sure we use this technology in the correct way to improve care for our patients," added Mathur.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

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Human-centered AI can improve the patient experience - Healthcare IT News