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DUBLIN--(BUSINESS WIRE)--The "Global Artificial Intelligence in Medical Imaging Market: Size, Trends & Forecast with Impact of COVID-19 (2022-2026)" report has been added to ResearchAndMarkets.com's offering.

Artificial intelligence (AI) is a branch of computer science that aims to emulate human intelligence through intelligent systems such as image analysis and speech recognition. Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics.

The global artificial intelligence in medical imaging market can be segmented based on image acquisition technology (X-Ray, CT, MRI, Ultrasound Imaging, and Molecular Imaging); AI technology (Deep Learning and Other AI & Computer Vision); clinical application (Cardiology, Neurology, Breast, Pulmonology, Liver, and Rest of the Body); and end-user (Medical Institutions and Consumer Healthcare Environment).

COVID-19 has a positive effect on market growth. Attempts have also been made to identify various imaging features of chest CT, resulting in increased popularity for AI in the medical imaging market amid the pandemic. However, with COVID-19 cases on the rise across the world, emerging AI technologies are developed to support hospitals in scaling treatment in the second wave. It also highlights the significance of expanding the use of AI and machine learning in imaging, with the dual goals of improving diagnoses and improving clinician well-being and job security.

The global AI in medical imaging market has increased during the years 2019-2021. The projections are made that the market would rise in the next four years i.e. 2022-2026 tremendously. The global AI in medical imaging market is expected to increase due to the increasing burden of chronic diseases, increasing health spending, increasing funding in AI, increasing government expenditure and policy support, etc. Yet the market faces some challenges such as development hurdles, the black-box nature of AI, etc. Moreover, the market growth would succeed by various market trends like increasing diversity in training datasets, detecting multiple diseases from a single image, high image resolution to maximize algorithm performance, etc.

The global AI in the medical imaging market is fragmented. The key players of the global AI in the medical imaging market are IBM (IBM Watson Health), Butterfly Network, Inc., Gauss Surgical, Inc., and Arterys are also profiled with their financial information and respective business strategies.

Market Dynamics

Drivers

Challenges

Trends

Company Coverage:

For more information about this report visit https://www.researchandmarkets.com/r/eq3eya

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Global Artificial Intelligence in Medical Imaging Market (2022 to 2026) - Size, Trends & Forecast with Impact of COVID-19 - ResearchAndMarkets.com...

The Ministry of Agriculture and Forestry plans to implement artificial intelligence (AI) technology to tackle forest fires, which destroyed large swaths of land last year.

AI will be used in the Remote Smoke Detection-Early Fire Warning System developed by the ministry. It will enable a faster response to fires. Forestry Minister Bekir Pakdemirli said the technology will be used in cameras set atop watchtowers in the forests. In an interview published by Yeni afak newspaper on Wednesday, he stated that cameras can detect smoke from a distance up to 20 kilometers (12.4 miles) through smoke perception, and the new system would reduce the detection time to two minutes.

The system is currently installed in Antalya and Mula, two Mediterranean provinces that lost hundreds of acres of forests to devastating wildfires in the summer of 2021, one of the worst and deadliest outbreaks in the region. AI enables us to keep track of the smoke and deploy our teams as soon as possible, Pakdemirli said.

The ministry has 76 smart watchtowers, entirely operated without staff and 103 towers installed with cameras. Cameras, through AI and machine learning, are able to send alarm signals to authorities, via text or multimedia message, upon detection of smoke. Every tower can scan an area of up to 50,000 hectares in two minutes and can send exact coordinates of the fire.

Forest fires, worsened by the ongoing climate crisis, are a major concern for Turkey, which has expanded its forest cover in the past two decades. President Recep Tayyip Erdoan said on Monday after a Cabinet meeting that they were working to boost infrastructure to fight forest fires. We will increase the number of domestically manufactured unmanned aerial vehicles (UAVs) to eight, the number of firefighting planes to 20 and helicopters to 55, Erdoan said.

Turkey suffered from at least 2,105 forest fires last year, though the worst was in Antalya and Mula. Strong winds and extreme temperatures hampered efforts to douse the fires. The country witnessed an unprecedented surge in forest fires starting from the last week of July, a period with the highest number of almost simultaneous forest fires. It took around two weeks for authorities to put out all 240 wildfires that had raged across the country forcing the evacuation of hundreds of people.

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Turkey taps artificial intelligence in its fight against wildfires | Daily Sabah - Daily Sabah

By Richard Shearer, CEO of Tintra PLC

In the modern banking world new technologies play a widely reported role in anti-money laundering (AML) protocols preventing financial crime however it is important that we do not overlook technologys potential for establishing financial innocence.

To businesses and institutions operating in and between developed markets, whose international transactions are fast and painless, this sentiment may seem counter intuitive. AML compliance is necessary for regulatory reasons, and catching out bad actors is, of course, a primary goal of any business but why should we view AML technology through the lens of establishing innocence?

This is a question which emerging market corporates will have no difficulty answering if they have ever attempted to interface with counterparts in developed markets.

Entities based in emerging markets are often tarred with the brush of AML risk due to their geography and unrelated to their specific business, and consequently such organisations find international transactions lengthy, arduous and expensive as they navigate an AML compliance process that operates from a base level that is an unfair assumption of their risk.

As such, in my view, embracing advances in technologies such as natural language processing (NLP) and machine learning (ML) is essential not only for financial services firms looking to enhance their ability to properly mitigate, but to progress the much bigger, and indeed more noble, goal of removing the biases against emerging markets, nationalities or cultures that currently colour the AML landscape.

How then, can NLP and ML technologies help, not only in addressing financial crime, but in creating an environment where those in emerging markets with upstanding credentials are treated and serviced free from these baked in prejudices?

Intelligent machines

Its worth taking a moment to define these terms.

Natural Language Processing pertains, in broad terms, to anything related to processing language. As such, NLP varies in terms of complexity it may be employed for tasks like term frequency, calculating how often a given word appears in a text, but NLP can equally be used for the purposes of translation; classifying the sentiment of a piece of text; or even detecting sarcasm, irony, and fake news in a social media context.

In order to perform the more complex tasks in this spectrum however, machine learning may also be required.

Machine Learning describes a variety of artificial intelligence (AI) with an emphasis on allowing machines to learn in a similar manner to humans, through a mix of data and algorithmic methods.

ML differs from traditional programming. Traditional programs see a solution to a problem defined through hand-crafted rules that are implemented in computer code. In ML, by contrast, the algorithm itself learns those rules and, by extension, how to solve the problem by analysing data.

This principle makes ML considerably more powerful than traditional programming, since it is capable of learning a complicated sets of rules that are impossible to define manually.

AML applications of these Technologies

In the context of AML practices, its not difficult to see the appeal of technology like this.

After all, manual investigations into potentially rogue activities are lengthy processes which involve employees investigating vast swathes of transaction histories and other information and often only happen after the event.

This process is made all the more difficult to manage for financial institutions when a large number of suspicious incident alerts are often false alarms. But each potential issue must be investigated with the same vigour to ensure a robust AML framework.

By contrast NLP/ML allows financial institutions to automate these processes the more sophisticated solutions, that my team and I are very focused on, are capable of interpreting the vast amounts of text-based data that a human would otherwise need to analyse.

These systems are able to recognise patterns and relevant information, consider appropriate context and cross reference faster and more accurately than a human, or indeed teams of humans, may overlook.

Crucially for me, NLP/ML performed by intelligent machines capable of learning can potentially undertake these tasks at the same time as neutralising human bias, which has promising implications for organisations and individuals in emerging markets who face these preconceptive biases frequently.

Less human, more humane

This application of NLP/ML has a range of benefits for all stakeholders, not least with reductions in the level of false positives representing savings in time and money for financial services companies.

There is, however, equal value to be found in NLP/ML tools which bring this power to bear on addressing the inequities that currently prevent frictionless transactions between these markets.

This piece began with reference to establishing cases of financial innocence as well as financial crime and, while NLP/ML makes this possible, it would be wrong to assume that such tools will magically resolve the issue of AML bias.

As such, establishing innocence isnt just a different perspective on the benefits of NLP/ML solutions its an ethos that I believe should be actively pursued by financial services businesses as our global economy becomes more and more integrated.

Removing human prejudice from the decision-making process is vital, but a truly fair approach can only be achieved when the creators of these solutions acknowledge that the prejudice exists in the first place.

After all, NLP/ML is entirely subject to bias or algorithmic unfairness,. A good example taken from research published in ACM Computing Surveys is a piece of software called COMPAS, used by US judges to assess offenders risks of reoffending, which was found to exhibit bias against African-American individuals illustrating clearly that human prejudice can inflect algorithmic decision-making. To make the technology better we need to be better, is may be one way of thinking about it.

This kind of example gives food for thought. If NLP/ML tools are trained without thought being given to how to eradicate bias in an AML context, then well be left with intelligent machines that simply replicate that bias meaning that prejudice will be automated rather than eliminated! A terrifying concept and one fraught with complex ethics.

The next step

The financial services sector is in the midst of digital transformation and as such the time is ripe to seize the wheel and ensure, as we embrace more sophisticated tech solutions, that the journey ends at a fair and equitable destination no matter where a given transaction takes place.

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Artificial Intelligences Role in Banking 3.0 - Global Banking And Finance Review

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Artificial intelligence goes far beyond just music or clothing recommendations which poses unforeseen risks for all of us. In his new book The Loop, NBC News Technology correspondent Jacob Ward warns AI is eroding our ability to make decisions on our own. He tells Ali Velshi that companies are deploying these pattern recognition systems to figure out what you and I are going to do nextthe capacity for manipulation and even predatory tactics is enormous. He adds AI offers unscrupulous businesses the opportunity to make incredible money off us by just playing to our worst instincts.Jan. 30, 2022

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Your Brain on AI: Artificial Intelligence is creating a world without choices - MSNBC

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Supply Chain Management Market by Technology, Processes, Solutions, Management Function (Automation, Planning and Logistics, Inventory, Risk), Deployment Model, Business Type and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report provides detailed analysis and forecasts for AI in SCM by solution (Platforms, Software, and AI as a Service), solution components (Hardware, Software, Services), management function (Automation, Planning and Logistics, Inventory Management, Fleet Management, Freight Brokerage, Risk Management, and Dispute Resolution), AI technologies (Cognitive Computing, Computer Vision, Context-aware Computing, Natural Language Processing, and Machine Learning), and industry verticals (Aerospace, Automotive, Consumer Goods, Healthcare, Manufacturing, and others).

This is the broadest and most detailed report of its type, providing analysis across a wide range of go-to-operational process considerations, such as the need for identity management and real-time location tracking, and market deployment considerations, such as AI type, technologies, platforms, connectivity, IoT integration, and deployment model including AI-as-a-Service (AIaaS).

Each aspect evaluated includes forecasts from 2022 to 2027 such as AIaaS by revenue in China. It provides an analysis of AI in SCM globally, regionally, and by country including the top ten countries per region by market share.

The report also provides an analysis of leading companies and solutions that are leveraging AI in their supply chains and those they manage on behalf of others, with an evaluation of key strengths and weaknesses of these solutions.

It assesses AI in SCM by industry vertical and application such as material movement tracking and drug supply management in manufacturing and healthcare respectively. The report also provides a view into the future of AI in SCM including analysis of performance improvements such as optimization of revenues, supply chain satisfaction, and cost reduction.

Select Report Findings

Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations.

Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable, and consistent fashion. Various forms of Artificial Intelligence (AI) are being integrated into SCM solutions to improve everything from process automation to overall decision-making. This includes greater data visibility (static and real-time data) as well as related management information system effectiveness.

In addition to fully automated decision-making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains). It is anticipated that virtual assistants in SCM will leverage an industry-specific knowledge database as well as company, department, and production-specific learning.

AI-enabled improvements in supply chain member satisfaction causes a positive feedback loop, leading to better overall SCM performance. One of the primary goals is to leverage AI to make supply chain improvements from production to consumption within product-related industries as well as create opportunities for supporting "servitization" of products in a cloud-based "as a service" model. AI will identify opportunities for supply chain members to have greater ownership of "outcomes as a service" and control of overall product/service experience and profitability.

With Internet of Things (IoT) technologies and solutions taking an ever-increasing role in SCM, the inclusion of AI algorithms and software-driven processes with IoT represents a very important opportunity to leverage the Artificial Intelligence of Things (AIoT) in supply chains. More specifically, AIoT solutions leverage the connectivity and communications power of IoT, along with the machine learning and decision-making capabilities of AI, as a means of optimizing SCM by way of data-driven managed services.

Key Topics Covered

1. Executive Summary

2. Introduction

2.1 Supply Chain Management

2.1.1 Challenges

2.1.2 Opportunities

2.2 AI in SCM

2.2.1 Key AI Technologies for SCM

2.2.2 AI and Technology Integration

3. AI in SCM Challenges and Opportunities

3.1 Market Dynamics

3.1.1 Companies with Complex Supply Chains

3.1.2 Logistics Management Companies

3.1.3 SCM Software Solution Companies

3.2 Technology and Solution Opportunities

3.2.1 Leverage Artificial Intelligence (AI)

3.2.1.1 Integrate AI with Existing Processes

3.2.1.2 Integrate AI with Existing Systems

3.2.2 Integrate AI with Internet of Things (IoT)

3.2.2.1 Leverage AIoT Platforms, Software, and Services

3.2.2.2 Leverage Data as a Service Providers

3.3 Implementation Challenges

3.3.1 Management Friction

3.3.2 Legacy Processes and Procedures

3.3.3 Outsource AI SCM Solution vs. Legacy Integration

4. Supply Chain Ecosystem Company Analysis

4.1 Vendor Market Share

4.2 Top Vendor Recent Developments

4.3 3M

4.4 Adidas

4.5 Amazon

4.6 Arvato SCM Solutions

4.7 BASF

4.8 Basware

4.9 BMW

4.10 C.H. Robinson

4.11 Cainiao Network (Alibaba)

4.12 Cisco Systems

4.13 ClearMetal

4.14 Coca-Cola Co.

4.15 Colgate-Palmolive

4.16 Coupa Software

4.17 Descartes Systems Group

4.18 Diageo

4.19 E2open

4.20 Epicor Software Corporation

4.21 FedEx

4.22 Fraight AI

4.23 H&M

4.24 HighJump

4.25 Home Depot

4.26 HP Inc.

4.27 IBM

4.28 Inditex

4.29 Infor Global Solutions

4.30 Intel

4.31 JDA

4.32 Johnson & Johnson

4.33 Kimberly-Clark

4.34 L'Oreal

4.35 LLamasoft Inc.

4.36 Logility

4.37 Manhattan Associates

4.38 Micron Technology

4.39 Microsoft

4.40 Nestle

4.41 Nike

4.42 Novo Nordisk

4.43 NVidia

4.44 Oracle

4.45 PepsiCo

4.46 Presenso

4.47 Relex Solution

4.48 Sage

4.49 Samsung Electronics

4.50 SAP

4.51 Schneider Electric

4.52 SCM Solutions Corp.

4.53 Splice Machine

4.54 Starbucks

4.55 Teknowlogi

4.56 Unilever

4.57 Walmart

4.58 Xilinx

5. AI in SCM Market Case Studies

5.1 IBM Case Study with the Master Lock Company

5.2 BASF: Supporting smarter supply chain operations with cognitive cloud technology

5.3 Amazon Customer Retention Case Study

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Global Artificial Intelligence (AI) in Supply Chain Management (SCM) Market 2022-2027 - Solutions as a Whole Will Reach $16.7B Globally by 2027 -...