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CHICAGO, Dec. 1, 2021 /PRNewswire/ -- In-depth analysis and data-driven insights on the impact of COVID-19 included in this global healthcare artificial intelligence (AI) market report.

The healthcare artificial intelligence (AI) market is expected to grow at a CAGR of over 46.21% during the period 20202026.

Key Highlights Offered in the Report:

1. Software & services in healthcare AI is expected to witness high growth rate due to the evolution in digital health technologies such as EMR/EHR, telehealth, e-prescribing in healthcare services. The software and services segment accounted for 73.92% of healthcare AI market share in 2020.

2. Artificial intelligence in drug discovery and clinical decision-making leads to new path of speedy workflow in pharmaceutical drug & dosage management. This emerging trend of advancements in drug discovery is incorporated widely as it is cost and time efficient.

3. The growth and acceptance of AI in hospital workflow management witnessed streamlined and efficient patient care offered by the healthcare facilities. Hospital workflow management accounted for 38.41% of healthcare AI market in 2020 and expected to retain its market dominance in coming years.

4. North America captured a dominant position in the market and generated maximum revenue in compared to rest. Additionally, Europe and APAC are likely to see the top line growth in adoption of AI and healthcare information technology solutions in healthcare facilities.

5. Vendors from the market are rising the investment in digital solutions for healthcare. In recent years, the number of collaborations between IT companies and healthcare providers have been made to adopt new technologies and incorporating to better outcomes.

6. Machine learning is widely applied in and adopted in the healthcare services because of its algorithmic more accurate results. In application segment machine learning accounted a major share of 41.16% of healthcare AI market in 2020.

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Healthcare Artificial Intelligence (AI) Market Segmentation

Healthcare Artificial Intelligence (AI) Market by Category

Healthcare Artificial Intelligence (AI) Market by Application

Healthcare Artificial Intelligence (AI) Market by Technology

Healthcare Artificial Intelligence (AI) Market by End-users

Healthcare Artificial Intelligence (AI) Market Dynamics

Global key vendors from the pharmaceutical and biotech industry, such as Amgen, BASF, LEO, Eli Lilly, AstraZeneca, Bayer, Novartis, J&J, and Merck, are now a part of machine learning for pharmaceutical discovery. These vendors are making remarkable growth as AI-driven solutions are easy for the user interface and are creating analytical bookmarks on key drivers in sales to determine investments. For example, the collaboration of GSK with Exscientia identified a small compound for targeted therapeutics and its characteristics toward the specific target using AI platforms. Gilead Sciences and Insitro partnered to research on non-alcoholic steatohepatitis patients. Using machine learning and human genetics, Insitro identified discovery & development in therapeutics.

Key Drivers and Trends fueling Market Growth:

Healthcare Artificial Intelligence (AI) Market Geography

North America captured a dominant position in healthcare artificial intelligence (AI). The presence of a large patient population, coupled with better adoption of digital healthcare with the latest advancements in artificial intelligence (AI), is the primary factor for its high market share. The strong presence of key healthcare IT players is also another reason for the high uptake of healthcare artificial intelligence (AI) in North America. The rise in the adoption of AI technologies in drug discovery & development and patient-centric treatment procedures are expected to drive the market growth during the forecast period. New artificial intelligence (AI) technologies can help companies create more value for their patients and communities by turning time-consuming, labor-intensive, and often ineffective functions into actionable insights for better results. Forward-thinking hospital and healthcare system executives view AI as the most effective path to a more productive, efficient, and better performing healthcare organization.

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Healthcare Artificial Intelligence (AI) Market by Geography

Major Vendors

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Healthcare Artificial Intelligence (AI) Market Size to Reach Revenues of USD 44.5 Billion by 2026 - Arizton - PRNewswire

AI will boost the security of companies and help keep out cyber attacks according to the expert

Dr Pius Gadossey, Lecturer, Lancaster University says the use of Artificial Intelligence (AI) in the financial space will boost the security companies by alerting institutions of any discrepancies or unusual activity for prompt actions.

Dr Gadossey said the AI in the financial sector ensured that clients and financial institutions' monies and their personal information were safe and secured as well as prevented cyber-attacks.

"AI is not biased and can decide on loan eligibility quickly and more accurately", he said.

The Lecturer said this on Saturday at a lecture and graduation ceremony of which eight members were Chartered as Compliance and Cyber Analyst.

The programme was organised by the Institute of Compliance and Cyber Studies (ICCS).

AI is the ability of a computer programme or a machine to think or learn. It provides human-like interventions with software and offers decision support for specific tasks.

Speaking on the "Relevance of AI in the Financial Industry", he said banks were using machine learning algorithms not only to determine a person's loan eligibility but also to provide personalized options.

"Machine learning can help experts use data to project trends, identify risks, conserve manpower and ensure better information for future planning," he said.

Dr Gadossey said AI fraud detection systems analyses a person's buying behaviour and trigger an alert if something seems out of the ordinary or contradicted traditional spending patterns.

"The use of applications like chatbot and virtual have reduced the need to spend time on the phone waiting to speak with a customer service representative", he said.

The Bank of America chatbot "Erica" has served more than 10 million users. As of mid-2019, Erica was able to understand almost 500,000 question variations.

Mr Eebenezer Domesew, Board Member of Institute of Compliance and Cyber Studies touching on the "Relevance of Risk-Based Auditing in the Financial Industry" said competitiveness had been toughened in the face of the increased presence of multinational companies which have embraced the advantages of technology.

He stated that adopting risk management practices gives higher financial performance and a highly competitive edge in the world market.

The graduands were presented with a certificate for successfully graduating from the course.

Mr Jude Apoore Azure won the best Governance and Ethics award while Mr Richard Nyarko emerged as the Audit and Tax Compliance award winner.

Originally posted here:
Artificial Intelligence in financial sector will boost security of companies - GhanaWeb

Sydney and Newcastle are deploying artificial intelligence and edge computing technology in a trial to improve customer journey, safety, and increase transport service reliability.

Minister for Transport and Roads Rob Stokes says the transport for NSW trials are connecting several buses, ferries, and light rail vehicles, and then using real-time data to help identify improvements for customers.

Weve partnered with Cisco to investigate how a real-time view of vehicle supply and customer demand and performance can guide future network decisions and monitor road conditions to identify where repair work is needed, Stokes says.

Pitt Street near Central Station is using artificial intelligence, Wi-Fi, and edge computing to capture real time data and identify high-risk events.

Well be able to keep a close eye on vehicle movements as more commuters and pedestrians head back to Sydneys busiest transport hubs as COVID-19 restrictions continue to ease, Stokes claims.

Transport for NSW deputy secretary of customer, strategy, and technology Joost de Kock says road user movements are tracked at several intersections in Newcastle using intelligent sensors to help improve overall road safety.

In another trial, we are also using advanced sensors on buses, ferries, and light rail to provide instant feedback on the comfort of customer journeys and improve real-time customer information, de Kock says.

Buses equipped with this technology can also monitor asset and road conditions and provide us with real-time information on vehicles.

Both cities are also trialling Ciscos technologies including:

Internet of Things (IoT) - enabling physical objects to be digitised and connected to the network via sensors

Edge Computing - taking real-time data from connected objects and allowing for faster decision making

Artificial Intelligence assists with data and automating the process, which will help to transform the data into useful insights.

Cisco Australia and New Zealand general manager for transport and infrastructure Simon Young says Transport for NSW is the only agency in the world trialling their AI system.

These trials represent the strength of the partnership between Cisco and Transport for NSW to co-innovate and use technology to solve some of the most pressing and challenging problems facing transport agencies, Young says.

The cost of congestion in Sydney, the Hunter, and Illawarra is forecast to double to $15.7 billion by 2031, according to a 2019 report by Infrastructure Australia.

This puts Transport for NSW in a position to make this new technology invaluable by helping to support a more efficient road and transport network.

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About the position

The Department of Circulation and Medical Imaging has a vacancy for a PhD candidate. This position is a fixed term, full time (100 %) position for three years starting in autumn 2022.

For a position as a PhD candidate, the goal is a completed doctoral education up to an obtained doctoral degree.

The PhD candidate will develop and validate machine learning techniques for automatic assessment of digital lung cancer tissue sections. The main goal is to achieve more patient specific management through improved prognostic differentiation in lung cancer and to provide a deeper understanding of the pathology of the disease. The basis of the project will be digitized whole slide images fromestablished, large, well-described cohorts of lung cancer patients and existing softwaredeveloped as part of the project.

The research team is cross-disciplinary and is a collaboration betweenNTNU, St. Olavs hospital, Levanger Hospital and SINTEF

The position offers challenging and varied tasks in an environment characterised by active, committed and skilled researchers with both technological and clinical expertise.

The successful candidate will be reporting to the project manager, MD and Associate Professor Hanne Sorger.

Duties of the position

Required selection criteria

The appointment is to be made in accordance with the regulations in force concerningState Employees and Civil ServantsandRegulations concerning the degrees ofPhilosophiaeDoctor (PhD)andPhilosodophiaeDoctor (PhD) in artistic researchnational guidelines for appointment as PhD, post doctor and research assistant

Preferred selection criteria

Personal characteristics

We offer

Salary and conditions

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 491 200 per annum before tax, depending on qualifications and seniority. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is 3 years.

Appointment to a PhD position requires that you are admitted to the PhD programme inMedical Technologywithin three months of employment, and that you participate in an organized PhD programme during the employment period.

The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.

The position is subject to external funding.

It is a prerequisite you can be present at and accessible to the institution daily.

About the application

The application and supporting documentation to be used as the basis for the assessment must be in English.

Publications and other scientific work must follow the application. Please note that applications are only evaluated based on the information available on the application deadline. You should ensure that your application shows clearly how your skills and experience meet the criteria which are set out above.

The application must include the following:

If all,or parts,of your education has been taken abroad, we also ask you to attach documentation of the scope and quality of your entire education, both bachelor's and master's education, in addition to other higher education. Description of the documentation required can befoundhere. If you already have a statement fromNOKUT,pleaseattachthisas well.

Joint works will be considered. If it is difficult to identify your contribution to joint works, you must attach a brief description of your participation.

In the evaluation of which candidate is best qualified, emphasis will be placed on education,experienceand personal and interpersonalqualities.Motivation,ambitions,and potential will also countin the assessment ofthe candidates.

NTNU is committed to following evaluation criteria for research quality according toThe San Francisco Declaration on Research Assessment - DORA.

General information

Working at NTNU

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background.

The city of Trondheimis a modern European city with a rich cultural scene. Trondheim is the innovation capital of Norway with a population of 200,000. The Norwegian welfare state, including healthcare, schools, kindergartens and overall equality, is probably the best of its kind in the world. Professional subsidized day-care for children is easily available. Furthermore, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life and has low crime rates and clean air quality.

As an employeeatNTNU, you must at all times adhere to the changes that the development in the subject entails and the organizational changes that are adopted.

In accordance with The Public Information Act (Offentleglova), your name, age, position and municipality may be made public even if you have requested not to have your name entered on the list of applicants.

If you have questions about the position,please contact Associate professor at NTNU Hanne Sorger, telephone +4791816787, email hanne.sorger@ntnu.no and/or Chief Scientist at SINTEF Medical technology Thomas Lang, telephone +4790962913, e-mail thomas.lango@sintef.no

If you have any questions about the recruitment process, please contact HR consultant Julie Hoff, e-mail: julie.hoff@ntnu.no

Please submit your application electronically via jobbnorge.no with your CV, diplomas and certificates. Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of Master Diploma fromChina Credentials Verification (CHSI).

If you are invited for interview you must include certified copies of transcripts and reference letters.

Application deadline: 15.02.2022

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

The Faculty of Medicine and Health Sciences (MH) is one of NTNUs largest faculties with about 1800 employees (1300 full-time equivalents).

The Facultys main activities are education and research in close integration with St Olavs Hospital. For further information, see: https://www.ntnu.edu/mh.

Deadline15th February 2022EmployerNTNU - Norwegian University of Science and TechnologyMunicipalityTrondheimScopeFulltimeDurationTemporaryPlace of serviceya, Trondheim

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PhD Candidate, Artificial Intelligence for Diagnostic Analysis of Digital Lung Cancer job with NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU...

Although traffic is a challenge in many cities across the globe, it is a challenge that can be managed and solved. Governments and city officials should work with the private sector to deploy intelligent solutions that not only improve the present state of roads and highways, but also prepare for the future.

Some of the solutions to traffic challenges lie in using artificial intelligence (AI), data collection and analytics, and sophisticated hardware that automates and streamlines processes to achieve high levels of accuracy, safety, and reliability.

Coupled with existing systems comprised of network cameras and monitoring solutions, the innovation and application of AI heralds a new era in traffic management.

Cities are getting bigger and smarter

Globally, our cities are growing and becoming more populous. The migration from rural to urban areas, combined with overall population growth, will see another 2.5 billion people living in cities by 2050, with 90% of this increase taking place in Africa and Asia, as per a United Nations report. This means more people travelling on more roads, and without smart interventions, this could mean more traffic.

In some areas, with this migration comes the emergence of smart cities urban areas that use ICT, AI, and data-related processes to maximise operations and investment.

In Abu Dhabi and Dubai two cities rated 28th and 29th respectively on the IMD Smart City Index 2021 traffic congestion is not considered a problem among residents. Such a result is, in part, thanks to the adoption of AI processes to deal with traffic and road management.

Managing traffic and the challenges thereof

Traffic management centres (TMCs) are the central nervous systems used for monitoring and managing traffic in an area. Imagine sprawling rooms with hundreds of surveillance cameras watching intersections, highways, and other road infrastructure for congestion, accidents, and the like to allow for quick-time responses from officials.

However, traditional TMC setups only allow for a certain level of monitoring and oversight.

Intelligent solutions, comprising both hardware and software, can fill these gaps.

Quality hardware is the first step

The strength and effectiveness of a TMC is indicated by its ability to collect data, and the application of AI requires up-to-date and robust hardware for it to achieve its full capability.

Centres use an extensive network of video management systems (VMSs) to monitor infrastructure and capture incidents in real time. This includes a range of fixed-point, panoramic, and thermal cameras hardware thats built to perform regardless of factors such as environment, object layout, and time of day.

The application of AI

To address traffic challenges, hardware is coupled with advancements in machine learning (ML), which focuses on gathering and analysing data to identify and replicate patterns in behaviour. By design, roads are ideal for this process as users conform to its rules, producing data which can be used to track deviations and create optimised models.

Deep learning (DL) takes this a step further. A sub-field of ML, DL uses raw data to automatically determine features that distinguish different sets of data from one another. The processing of data in this way does not need human intervention.

What does this look like in the context of traffic management? It automates several processes and decreases the necessity for human intervention or oversight. Data can be used to identify weak or exposed points in infrastructure or throughout the overall scope of surveillance. Roads are made safer through quicker response times from emergency services and officials.

Edge computing and edge-based analytics

Cameras capture a lot of data. A network of cameras tied to a VMS captures even more data, and with this comes the necessity of cloud or server-based data storage and processing. Edge computing offers an alternative, more effective solution to constantly transfer data from devices to the cloud.

With edge computing, captured data is analysed closer to its point of origin rather than having to go back-and-forth between servers and central data points.[1] The localisation of this process results in less dependency on other infrastructure and less time is lost due to factors such as network latency.

Urban populations may continue to grow, but traffic problems do not need to accompany this growth. Leaders can harness the power of AI combined with smart surveillance hardware as we build the cities of the future. Cities and urban areas stand to benefit from the application of AI in this manner as we head into a smart future together.

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Artificial Intelligence key to traffic solutions in cities of the future - Construction Week Online