Category Archives: Artificial Intelligence

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.

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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/

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

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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

Artificial intelligence in cybersecurity is presently gaining traction to secure information, and AI technology is proficient at rapidly investigating millions of data sets and tracking down a wide-ranging variety of cyber threats such as phishing attack, malware, and viruses. Adoption of AI in cybersecurity is expected to increase among end-users to resolve the issue of security and identify new varieties of attacks that can take place anytime; therefore, the adoption rate ofartificial intelligence in cybersecurity marketis increasing consistently

Artificial intelligence endeavors to replicate human intelligence. AI has vast potential in cybersecurity. If connected correctly, artificial intelligence systems can be trained to produce alerts for malware, identify new types of threats, and defend sensitive data for organizations. Increase in number of cyber-attacks would mean that a security expert in the company would not be able deal with the issue, and some of the threats would therefore naturally go unnoticed, which can cause a huge damage to the network. Therefore, artificial intelligence in cybersecurity can play an important role to guard the companys network and sensitive data.

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Artificial Intelligence in Cybersecurity Market: Key Drivers

Artificial intelligence in cybersecurity systems can deliver the latest information of global as well as business-specific risks to formulate vigorous prioritization results based not only on what is most likely to be used to attack your system but what might be used to attack your systems

Artificial intelligence in cybersecurity technology is intelligent, and it uses its ability to advancenetwork securityover time. It employs deep learning and machine learning to learn a business networks performance over time. It identifies patterns on the network and automatically clusters them.

Artificial intelligence in cybersecurity technology helps evaluate systems rapidly to identify the business network and weak point in the computer system. It also helps businesses to focus on important security task in order to easily manage vulnerability and protect business systems in time.

Artificial intelligence in cybersecurity ensures the security of technological innovations and critical infrastructure; consequently, companies are increasing their AI cybersecurity budgets significantly, supporting business approaches with cybersecurity plans, and creating cyber alertness programs for customers and employees.

Impact of COVID-19 on Artificial Intelligence in Cybersecurity Market

Increase in cases of COVID-19 across the globe is resulting in an economic slowdown. Developed countries are strongly affected by the pandemic. Integration of artificial intelligence in cybersecurity helps data centers, cloud systems, and digital devices, so there is a huge demand for artificial intelligence in cybersecurity

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North America to Hold Major Share of Artificial Intelligence in Cybersecurity Market

In terms of region, the global artificial intelligence in cybersecurity market can be divided into North America, Europe, Asia Pacific, Middle East & Africa, and South America

North America was a dominant region of the global artificial intelligence in cybersecurity market primarily owing to the presence of developed economies, such as the U.S. and Canada. Moreover, the high rate of adoption of artificial intelligence in cybersecurity by government agencies, financial institutes, and banks in the region face various challenges related to cyber threats and several big companies are based in North America are dealing in the artificial intelligence in cybersecurity market.

The global artificial intelligence in the cybersecurity market in Asia Pacific is anticipated to expand at a rapid pace during the forecast period. Developing countries in the region are mostly focused on the implementation of new technologies, and countries like India, China, and Japan are emphasizing on cybersecurity technology in various sectors.

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Key Players Operating in Global Artificial Intelligence in Cybersecurity Market

Intel Corporation

Intel Corporation specializes in the design and manufacture of integrated processors, platforms, chipsets, circuits, and software solutions, such as Intel security, and embedded software. The company offers a wide range of products including desktops, tablets, SSDs, boards & kits, server products, Intel gateways, modems, and radio frequency transceivers. It provides solutions for embedded applications for healthcare, automotive, energy, and retail sectors.

SAP SE

SAP SE is a global technology company that provides enterprise application software for industries and companies across diverse sectors. SAP SE offers solutions for various businesses, including commerce, finance, asset management, manufacturing, human resources, supply chain, and procurement. The company also offers analytic solutions such as agile visualization, and business intelligence and cybersecurity for information and data needs.

Other key players operating in the global artificial intelligence in cybersecurity market include NVIDIA Corporation, Xilinx, Inc., Microsoft Corporation, International Business Machines Corporation, Samsung Electronics Co., Ltd., ThreatMetrix, Inc., Vectra AI, Inc., Palo Alto Networks, Inc., SparkCognition, Inc., Acalvio Technologies, Amazon Web Services, Inc., and Securonix, Inc.

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Artificial Intelligence in Cybersecurity Market Sales to Witness Significant Growth in the Near Future, TMR - Digital Journal

HUMBOLDT COUNTY, Calif.(KIEM)- During extreme weather, determining wildfire smoke from fog and other false indicators is crucial. This is why PG&E is testing artificial intelligence and machine-learning capabilities. In the growing network of high-definition cameras across Northern and Central California to see how it can enhance fire-watch and response capabilities.

One of the tools that PG&E have is in mitigating wildfire threats and producing the threat of wildfires are these HD fire watch cameras. We have 487 of them across our service territory, and 11 of them are in Humboldt County. Anybody can go on alertwildfire.org and see the live picture of these cameras, said Deanna Contreras, Spokesperson with PG&E.

None of the11 PG&E cameras located in mountain tops in Humboldt Countyhave the artificial intelligence software setup. They have collected the data from the ones with the software and are trying to figure out how to make the current IA software smarter and better.

We know it works. We know it rules out false positives, haze, fog, and smoke, and it lets us know that it spots smoke, said Deanna Contreras

The cameras provide 360-degree views and can be viewed by anyone through alertwildfire.org. By the end of 2022, the PG&E plans to have approximately 600 cameras installed to provide an ability to see in real-time more than90% of the high fire-risk areas.

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PG&E is using artificial intelligence to enhance fire-watch and response capabilities - KIEM

Prof. Anubha Gupta, Professor IIIT-Delhi talks about the role of artificial intelligence in cancer detection and highlights her teams research on this

Multiple Myeloma (MM) is a type of blood cancer that is owing to the malignancy of plasma cells. The overall survival of patients after being diagnosed with MM ranges from 6 months to more than 10 years. The variability in the outcome is an implication of the underlying biological heterogeneity. The current risk predictors of MM have been established on western populations and do not integrate ethnicity-specific information, the impact of which on disease biology cannot be overlooked.

India is ethnically diverse and has wide disparity in its healthcare infrastructure. A large number of cancer patients are initially diagnosed at peripheral hospitals and then seek specialized cancer care at advanced cancer centres. Staging of cancer is important in assessing the risks of progression, morbidity, mortality and to decide the appropriate treatment. The investigations done at the initial presentation of the disease are crucial in staging of the cancers. It is, therefore, important to develop staging systems that are based on simple tests that are widely available and yet have strong impact on disease so as to be informative of the cancer stage.

In this context, a team of researchers led by Dr Ritu Gupta, Professor, Laboratory Oncology Unit, Dr B.R.A. IRCH, AIIMS, New Delhi and Prof. Anubha Gupta, Deptt. of ECE and member, Centre of Excellence in Healthcare (CoEHe) IIIT-Delhi, did a systematic evaluation of more than 1000 Indian patients of MM. The team established the impact of ethnicity on MM risk prediction and developed two efficient and robust Artificial Intelligence (AI)-enabled risk-staging systems, namely, 1) Modified risk staging (MRS) system for patients in whom high-risk cytogenetic aberrations (HRCA) based on genomic data is not available and 2) Consensus based risk-staging system (CRSS) to establish the biological relevance of the risk predictions in patients for whom the genomic data is available. The team identified disease-specific parameters and assigned them weightage using AI and incorporated them into the risk stage prediction based on their ability to contribute to the risk of the disease.

The MRS is based on patient and disease-specific parameters of age, serum levels of albumin, creatinine, beta 2 microglobulin, calcium, and haemoglobin. All of these six parameters are tested on blood and are widely available at the level of district hospitals. The simplicity of the method allows for staging of the disease for almost all the patients diagnosed with multiple myeloma in our country. The CRSS is an advanced model which includes additional three genetic parameters and can be used in patients in whom cancer genetic testing is available.

Both these MRS and CRSS works have been published in renowned peer-reviewed international journals and were compared with the current international staging system, i.e., the revised International Staging System (R-ISS). Full details are available for MRS work in the publication in the journal of Translational Oncology, Elsevier and for CRSS work in the publication in the journal of in Frontiers in Oncology.

We used curated dataset of more than 1000 patients of multiple myeloma collected over a period of five years and another dataset of 900 patients from the American population curated by the Multiple Myeloma Research Foundation (MMRF), USA. Both the risk prediction and staging systems developed by us performed better than RISS in Indian cohort and also improved risk stratification in the American dataset.

We have designed simple online tools to allow automated calculation of MRS and CRSS. One can find out the stage of the disease by feeding the values of the laboratory test results and age of the patient; and generates predictions for survival for the particular patient case.

Our CRSS work discovers changes in cut-offs in Indian patients from the established cut-offs of prognostic features and highlights the need for focused research to identify the differences and unique features of Indian patients with cancers for better risk stratification to decide on appropriate treatments. This work establishes novel robust risk-staging models that can be widely employed in India with its existing diversity and disparity in the health care infrastructure. As of now, the proposed calculators are validated for Indian population. In future, this concept can be used to develop risk stratification models for specific ethnic groups across the globe.

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Artificial intelligence as an efficient tool in cancer detection - Express Healthcare

Imagine youre planning a vacation for your family. Youve spent a generous amount of time researching destinations, booking flights, securing a car rental, and finally, the hotel. After thoroughly researching all of your options, you settled on a property and pick up the phone. After a few rings, you hear a voice on the other end of the line, and you immediately tense upa voice bot. What could have been a quick and painless phone call turns into a one-sided conversation that seems to take you in circles until finally, youre able to talk to a human on the other end of the line.

In 2021, customers value a personal connection, but the convenience of a voice bot is hard to beat for some businesses that just dont have the manpower to handle their current call volume. Is there a better solution for voice bots? To fully understand how Artificial Intelligence can fit into the hospitality industry, we must first understand how it has failed us thus far.

Voice bots are software used in call centers of large companies to help customers navigate to their desired representative more naturally than a voice recording with keyed responses. Voice bots are powered by artificial intelligence and are known as Interactive Voice Response Systems or IVR for short.

Despite vast improvements in technology from the previous iterations of IVR that had customers listen to menus and press corresponding numbers on their keypads, the majority of consumers still seek to avoid voice bots whenever possible.

IVR systems seemingly appeared overnight and forced customers into a loop of long wait times and incoherent call and answer scenarios. However, IVR systems are widely used by industries across the globe to help companies cope with massive call volumes. It doesnt take an industry expert to point out whats wrong with current IVR systems.

Because IVR systems can't differentiate between types of calls, customers are forced into a cycle of repeating menus to help narrow down their reason for calling. This is an incredibly frustrating situation to be in for any customer, but older customers find it especially difficult to follow.

Unlike humans, IVR systems cannot provide a personalized call experience. Completely unaware of customers purchasing history, previous needs, or customer journey, callers are all forced to jump through the same hoops, again and again, each time they call.

IVR systems can only collect and store a limited amount of data, so returning callers will not have their progress saved. Additionally, the failure of IVR systems to collect data cripples a companys ability to make data-driven decisions based on their customers call experiences.

According to Vonage, an industry leader in cloud communication, 61% of customers feel that IVRs make for a poor experience. Additionally, the State of IVR in 2018 asserts that 83% of customers have abandoned a company altogether after reaching an IVRs menu of options. Customer service experts have since identified the error of mass implementation of IVR.

The last year and a half put IVR systems to the ultimate stress test, especially in the travel industry. When flights are canceled in mass, call volumes for airlines surge, and its clear that IVR systems are hurting the customer experience rather than simplifying it.

Its 2021, automated customer service experiences don't have to be so painstakingly miserable. Many corporations need some type of automated system to help process and sort callers. An investment in technology to create a customer-focused, alternative intelligence-powered voice bot is a feasible solution.

A voice bot with a focus on increasing response time, decreasing total call time, and quickly redirecting callers with an added component of a humanlike interaction is now a reality. Keep your eye out for our next story when we introduce Bella, The Virtual Hotel Agent.

Given its progressive approach to the voice channel in terms of performance, training, transparency, testing and the tools used to measure performance Travel Outlook Premium Hotel Call Center has become the premier voice reservations team in hospitality. Travel Outlook"s valued client list includes Viceroy Hotel Group, Outrigger, KSL Resorts, Proper Hospitality Group, Pacific Hospitality Group, Highgate Hotels, The Irvine Company, Catalina Island and many others. Travel Outlook"s team and approach increases sales conversion and helps to create more effective voice communication between hotels and their guests, resulting in improved social scores in addition to increased voice channel revenue. For more information, visit http://www.traveloutlook.com.

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Does Artificial Intelligence Have a Place in the Travel Industry? | By John Smallwood Hospitality Net - Hospitality Net

Starting today (14 December) in Moscow, the WHO European conference on tackling noncommunicable diseases through digital solutions brings together decision-makers and experts from across the WHO European Region to identify innovative ways to tackle chronic diseases that affect millions of people.

The growing burden of noncommunicable diseases (NCDs) in the European Region has called for new approaches to managing chronic conditions. COVID-19 has limited access to health services and placed a huge burden on economies; inspiring countries to look for digital solutions to improve the quality of health services, making them more responsive to peoples needs.

At the same time, decision-makers across the Region are searching for new ways to improve the prevention of NCDs and promote healthier lifestyles in general an area that requires further exploration.

A selection of stories from countries shows how digital solutions can benefit prevention and treatment of NCDs.

A national diabetes registry was first established in 2000 in Croatia. Called CroDiab, the registry is a web-based system for the collection of information on diabetic patients, which allows health professionals to focus on their individual needs and choose better treatment options.

CroDiabs data is collected from government registries and primary care and hospital reports. Since 2004, use of this digital database has been mandatory for all primary and secondary health-care physicians who have patients with diabetes in their care.

A national electronic cancer data collection system in Georgia makes the cancer screening, diagnosis and treatment process more efficient for patients and doctors, and allows the government to better devise cancer management strategies.

The Unified Electronic System for Cancer Data Collection registers every step in the cancer case management process. As a result, patients do not have to carry around their diagnosis papers when seeing different specialists everything is already in the system. Using this innovative tool, the countrys health professionals and authorities are able to better plan cancer management and choose the best practices.

In Slovakia, a new technology helps reduce the average time spent by a radiation oncologist in planning radiation therapy for patients by at least 30%.

The software tool uses artificial intelligence to automatically generate images within seconds from computerized tomography (CT) scans. This helps oncologists ensure that radiation therapy planning is optimal, with the least possible impact on the patient.

Many people with chronic conditions find it makes a huge difference to get support from others dealing with the same challenges. Recognizing this, the Elsa Science app was developed in Sweden to link up patients who wish to share their experiences, gain knowledge about their condition, and play an active part in their health care.

The first chronic condition the Elsa Science app is focusing on is rheumatoid arthritis. While using the app, people with this condition can share their health information with their rheumatology specialists or health facilities, and get support from their families and friends.

In the European Region, digital solutions are helping more and more people to enjoy and share the benefits of quality health care and to learn more about healthier choices and lifestyles.

The Moscow conference on digital solutions to tackle NCDs reflects the vision of the WHO European Programme of Work 20202025, and shares the hope that even struggling with the challenges of COVID-19, we are creating a better and healthier world to live in.

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Artificial intelligence and data technology provide smarter health care 4 solutions that have made a difference for noncommunicable diseases - World...

On 7 December 2021, the Council of Europe Project Strengthening Media Freedom, Internet Governance and Personal Data Protection SMIP-GE launched the Project Action in the field of Artificial Intelligence and Personal Data Protection, which will be implemented in cooperation with the State Inspectors Service of Georgia (national data protection authority). The Project Action envisages use of the Council of Europe expertise to build the capacity of the State Inspectors Service in the field of Artificial Intelligence. This involves research, training and policy development for the institution. As a result of the series of activities, the State Inspectors Service will have improved skills and knowledge to monitor Artificial Intellige nce tools vis--vis personal data protection in Georgia.

The Project Action launch was organised in the framework of the Council of Europe Project Strengthening Media Freedom, Internet Governance and Personal Data Protection financed under the Council of Europe Action Plan for Georgia 2021-2023.

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Launch of the Project Action in the field of Artificial Intelligence and Personal Data Protection - Council of Europe