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Global Hospital Artificial Intelligence Market report presents the market analysis on the basis of several factors. Report gives the in-depth analysis on the major countries of key regions where the market is growing. In addition, report describes the wide-ranging knowledge about the major companies in this industry and the key strategies accepted by them to survive and rise in the studied industry.

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Our Market professionals are working relentlessly to understand, assemble and timely deliver assessment on impact of COVID-19 disaster on many corporations and their clients to help them in taking excellent business decisions.

The key insights of the report:

The report includes profiles of leading companies in the Hospital Artificial Intelligence market. Some of the key players profiled include:

Intel (US)NVIDIA (US)Siemens Healthineers (Germany)Medtronic (Ireland)Micron Technology (US)IBM (US)Microsoft (US)Google Inc (US)Amazon Web Services (US)Medtronic (Ireland)Micron Technology (US)

AI in Hospital Management Breakdown Data by TypeHardwareSoftwareService

AI in Hospital Management Breakdown Data by ApplicationHealthcare ProviderPharmaceutical & Biotechnology CompanyPatientOthers

Hospital Artificial Intelligence Market Regional analysis includes:

The report firstly introduced the Hospital Artificial Intelligence Market basics: definitions, classifications, applications and market Overview; product specifications; manufacturing processes; cost structures, raw materials and so on. At that point it analyzed the worlds primary locale showcase conditions, counting the item cost, benefit, capacity, generation, supply, and request and advertise development rate and estimate etc. Within the conclusion, the report presented unused extend SWOT examination, speculation possibility examination, and speculation return examination.

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What You Can Expect from Our Report:

A report by HIM REPORTS Research studies the global Hospital Artificial Intelligence marketing details and offers a granular analysis of the different factors promoting or hindering the markets growth. It leverages market-leading explanatory instruments to gage the openings anticipating players. It moreover profiles the driving companies working in that and captures information on their incomes. Their item offerings are figured in to decide the advertise division.

Hospital Artificial Intelligence Market Report Summary

The report covers a wide run of ranges for way better experiences of the worldwide market and Market trends and forecasts. The report covers market patterns based on product types, application regions and key vendors. Market affecting variables such as drivers, controls and venture openings has been carefully detailed in this report. The examination of the market patterns, examination and figure has been done both at the large scale and micro level viewpoint. It further gives a total thought of the strategies received by major competitors within the business. Other significant variables, which works at the regional and worldwide level to affect the market trends has been included. These impacting variables are socio-political situation, environmental conditions, demography, legal organizations, and competitive environment of the region.

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In this study, the years considered to estimate the market size of Hospital Artificial Intelligence Market are as follows:

The Hospital Artificial Intelligence Market report includes overview, which deciphers value chain structure, industrial environment, regional examination, applications, market size, and forecast. Usually a most recent report, covering the current COVID-19 effect on the market. The pandemic of Coronavirus (COVID-19) has influenced each viewpoint of life all inclusive.

This has brought along a few changes in market conditions. The quickly changing market situation and initial and future evaluation of the affect is secured within the report. The report gives an overall analysis of the market based on types, applications, regions, and for the forecast period from 2021 to 2028. It moreover offers investment opportunities and likely threats within the market based on an brilliantly investigation.

Table of Contents

1 Study Coverage

2 Executive Summary

3 Breakdown Data by Manufacturers

4 Breakdown Data by Type

4.1 Global Hospital Artificial Intelligence Market Sales by Type

4.2 Global Hospital Artificial Intelligence Market Revenue by Type

4.3Hospital Artificial Intelligence Market Price by Type

5 Breakdown Data by Application

5.1 Overview

5.2 Global Hospital Artificial Intelligence Market Breakdown Data by Application

6 North America

7 Europe

8 Asia Pacific

9 Central & South America

10 Middle East and Africa

11 Company Profiles

12 Future Forecast

13 Market Opportunities, Challenges, Risks and Influences Factors Analysis

14 Value Chain and Sales Channels Analysis

15 Research Findings and Conclusion

16 Appendix

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About HealthCare Intelligence Markets:

At HealthCare Intelligence Markets, we supply markets intelligence reports in the domain of personalized drugs & diagnostics after going through a rigorous research process. The healthcare industry is constantly evolving as trends are getting replaced at a rapid pace. These new trends along with the changing demands of patients and healthcare organizations, are collectively contributing to the development of the global healthcare industry. The reports made by us are updated on a regular basis to cover the latest developments in the industry. Our workforce is comprised of seasoned market research professionals who can also provide customized report as per the exclusive needs. HealthCare helps clients decode the future to be more successful and innovative.

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Continued here:
Hospital Artificial Intelligence Industry: 2021 Global Market Size, Share, Uses, Benefits, Trends, Growth Application, Key Manufacturers and 2028...

"This year's World Cancer Day's theme, 'I Am and I Will', is all about you and your commitment to act. The new state-of-the-art artificial intelligence integrated PET-CT scanner at Yashoda Hospital Somajiguda is one more step towards our commitment to early detection of Cancer. The new scanner is now two times faster than the old generation scanners primarily due to the advanced technology known as 'Time of Flight'. The scanner provides best quality images with reduced scanning duration and lesser radiation dose," said Dr. G. Srinivasa Rao, Director of Public Health & Family Welfare, Government of Telangana.

Yashoda Hospitals Somajiguda is well equipped with a comprehensive Nuclear Medicine set up providing services like PET-CT, Gamma camera imaging and radionuclide therapy under one roof. Apart from the newly upgraded imaging of FDG PET-CT, the department provides advanced and rare imaging like Ga-68 DOTA, Ga-68 PSMA, 18F DOPA PET-CTs, DAT imaging & WBC scans, apart from routine Gamma imaging like bone scan & renal scintigraphy.

"Yashoda Hospitals Somajiguda is one of the busiest and high volume centres of radionuclide therapies for thyroid cancer, neuroendocrine tumours, and prostate cancer. The Centre also provides rare therapies like radiosynovectomy for inflammatory joint disease. Patients not only from Telangana and Andhra Pradesh, but across India, visitus for these rare therapies. NextGen PET-CT is effective in the diagnosis of Cancer, Endocrine Abnormalities and Neurodegenerative Disease," said Dr. Lingaiah Amidayala, Director - Medical Services, Yashoda Hospitals Group, Hyderabad.

The Combined PET-CT Scan at Yashoda Hospitals, Somajiguda merges PET and CT images and provides detailed information about the size, shape and differentiating cancerous lesions from normal structures with accuracy. It is a diagnostic examination that combines two state-of-the-art imaging modalities and produces 3 dimensional (3D) images of the body based on the detection of radiation from the emission of positrons. It helps in early detection of cancer and any potential health problem that reveals how the tissues and organs are functioning by identifying a variety of conditions.

Dr. Hrushikesh Aurangabadkar and Dr. A Naveen Kumar Reddy, Consultants in Nuclear Medicine while explaining about the PET-CT said, "The cancer cells require a great deal of sugar, or glucose, to have enough energy to grow. PET scanning utilizes a radioactive molecule that is similar to glucose, called fluorodeoxyglucose (FDG). FDG accumulates within malignant cells because of their high rate of glucose metabolism. Once injected with this agent, the patient is imaged on the whole body PET scanner to reveal cancer growth, which are usually difficult to characterize by conventional CT, X-Ray, or MRI."

With this new technology, motion artifacts caused by respiration can be decreased and accurate diagnosis achieved.

The use of PET scans will also help the doctors to more accurately detect the presence and location of new or recurrent cancers.

Relevant Links: https://www.yashodahospitals.com/location/somajiguda/

Nuclear Medicine: https://www.yashodahospitals.com/specialities/nuclear-medicine-hospital-in-hyderabad/

About Yashoda Hospitals Hyderabad

Yashoda Group of Hospitals has been providing quality healthcare for 3 decades for people with diverse medical needs. Under astute leadership and a strong management, Yashoda Group of Hospitals has evolved as a centre of excellence in medicine providing the highest quality standards of medical treatment. Guided by the needs of patients and delivered by perfectly combined revolutionary technology even for rare and complex procedures, the Yashoda Group hosts medical expertise and advanced procedures by offering sophisticated diagnostic and therapeutic care in virtually every specialty and subspecialty of medicine and surgery. Currently operating with 3 independent hospitals in Secunderabad, Somajiguda and Malakpet and an upcoming hospital (currently under development) in Hi-Tech city, Telangana which is expected to be one of the largest medical facilities in India and will be spread over 20 lakhs sq. ft. with a capacity of 2000 beds. With a constant and relentless emphasis on quality, excellence in service, empathy, Yashoda Group provides world-class healthcare services at affordable costs.

Photo: https://mma.prnewswire.com/media/1433696/AI_PET_CT_Launched_Yashoda.jpg

SOURCE Yashoda Hospitals Hyderabad

View post:
'Artificial Intelligence' Integrated PET-CT launched at Yashoda Hospitals, Hyderabad on the occasion of World Cancer Day 2021 - PR Newswire India

Expectations are high that artificial intelligence will be a game changer for the military and it is, in fact, one of the Defense Department's top priorities.

"We're in the very early days of a very long history of continued very rapid development in the AI field," said William Scherlis, director of the Information Innovation Office at the Defense Advanced Research Projects Agency. He spoke yesterday at a virtual panel discussion at the Defense One Genius Machines 2021 summit.

There are a lot of moving parts to AI that must come together to make it all work for the warfighter, he said.

Components include, machine learning, symbolic reasoning, statistical learning, knowledge representation, search and planning, data, cloud infrastructure, algorithms and computing, he said.

"If you want to do strategy planning, then you're gonna have a mashup of machine learning with, maybe, game theory and a few other elements. So when we talk about AI, sometimes people are referring to just machine-learning algorithms and data and training. But in the systems engineering context, we're really talking about how to build systems that, that have elements of AI capability embedded within them," he said.

Scherlis discussed the history of AI, back to the 1940s and noted that there were three waves of development.

The first wave involved symbolic AI, which has explicit rules, such as if it's raining, then bring an umbrella, he said. Commercial income tax programs operate this way, using rules, logic and reasoning to reach a conclusion.

The second wave involved neural nets, which Scherlis refers to as statistical AI. Neural nets attempt to replicate higher-order human thinking skills, such as problem solving.

All AI relies on having good data. But although data is certainly important, the real game-changer for AI will be the third wave where symbolic is meshed with statistical to get the best of both worlds, Scherlis predicted.

"This is a wide open research area, but there's a lot of good work in this area and I think it's very promising," he said, referring to third wave research.

This third wave will need to focus on how AI systems interact with humans in a productive and symbiotic way, he said.

Warriors will have to understand what it's like to have an AI as a trusted team member, he said.

Currently, AI isn't yet ready for prime time, he said. It's still fragile, opaque, biased and not robust enough, which means it does not yet have trustworthiness.

"At DARPA, we have another number of programs that are, that are addressing these challenges," he added.

Read the original here:
Artificial Intelligence Is a Work in Progress, Official Says - Department of Defense

AI technique enables researchers to study data trends more quickly, improving prediction ability

Researchers are speeding up analysis of Arctic ice and snow data through AI.

January 27, 2021

Researchers at the University of Maryland, Baltimore County have developed a technique for quicker analysis of extensive data from Arctic ice sheets to gain knowledge of patterns and trends.

Over the years, vast amounts of data have been collected about Arctic and Antarctic ice. These data are essential for scientists and policymakers seeking to understand climate change and the current trend of melting.

Researchers Masoud Yari and Maryam Rahnemoonfar have utilized new AI technology to develop a fully automatic technique to analyze ice data. They describe the technology in the Journal of Glaciology. Their effort is part of the U.S. National Science Foundation's ongoing BigData project. The data build on new image-processing algorithms developed by John Paden at the University of Kansas.

"It is great to see the cooperation between computer revision and machine learning to help predict ice changes," said Sylvia Spengler, a program director in NSF's Computer and Information Science and Engineering Directorate.

For decades, researchers have kept close track of polar ice, snow and soil measurements, but processing the large volume of available data has proven challenging.

According to Rahnemoonfar, "Radar big data is very difficult to mine and understand just by using manual techniques." The AI techniques she and Yari are developing can be used to mine the data more quickly, to get useful information on trends related to the thickness of the ice sheets and the level of snow accumulation in a certain location.

The researchers developed an algorithm that learns how to identify objects and patterns in the Arctic and Antarctic data. An AI algorithm must be exposed to hundreds of thousands of examples to learn how to identify important elements and patterns. Rahnemoonfar and her team used existing Arctic data labeled as incomplete to train the AI algorithm how to categorize and understand new data.

The algorithm's training is not yet complete, as it will need to be scaled up over multiple sensors and locations to create a more accurate tool. However, it has already successfully begun to automate a process that was previously inefficient and labor-intensive.

The rapid expansion of AI technology to understand ice and snow thickness in the Arctic is allowing scientists and researchers to make faster and more accurate climate predictions. The rate at which Arctic ice is melting impacts sea level rise, and, if scientists are better able to predict the severity of the melting, society can better mitigate the harm caused by sea level rise, the researchers say.

Read more here:
Researchers speed up analysis of Arctic ice and snow data through artificial intelligence - National Science Foundation

Spurred by the demands of the COVID-19 pandemic, researchers at Princeton and Google are applying mechanical engineering and artificial intelligence to increase the availability and effectiveness of ventilation treatments worldwide.

Ventilators and their support equipment are expensive and complex devices that require expert attention from doctors and other highly trained medical workers. The devices must be carefully calibrated and monitored to ensure they are meeting a range of parameters pressure, volume, breath rate tuned to each individual patient. Often, these measures change during treatment, requiring further tuning.

If that monitoring and adjustment is handled by artificial intelligence, it could ease the burden on medical workers and allow ventilators to be deployed in areas with staffing shortages. That was the logic that led Elad Hazan, a professor of computer science and director of Google AI Princeton, and Daniel Cohen, an assistant professor of mechanical and aerospace engineering, to launch the project.

Graduate student Daniel Suo and senior Paula Gradu are part of a team of researchers using AI to improve the way ventilators assist patients.

Photo by

Aaron Nathans, Office of Engineering Communications

Modern ventilators seek to maximize clinical outcomes while at the same time protecting patients from excessive levels of pressure and volume, said Daniel Notterman, a board certified pediatric intensive care physician with experience managing patients with respiratory failure, who is also a lecturer with the rank of professor in molecular biology. Although conceptually simple, the regulation of ventilator performance is extremely complex. This effort provided the opportunity for experts in programming, engineering and clinical medicine to rethink many of the usual solutions, under the leadership of Professor Cohen.

Since the initial COVID-19 outbreak last spring, Cohens team had been working to design low-cost ventilators using readily available parts. Initially, Cohen met with Hazan to discuss a control system for the new design. But the researchers realized that artificial intelligence could improve controls for all ventilators, not just the type designed at Princeton.

The hypothesis is that applying AI tools can make systems more robust and safer, Hazan said.

Access to Cohens ventilator has been critical, Hazan said. The physics underlying breathing is complex, and breaking the fluid dynamics down into working equations is generally impractical and inaccurate. So instead of approaching the control problem through the physics of the lungs, the researchers ran experiments on the Cohen teams ventilators and applied machine learning to uncover patterns in the data that would guide the safe and effective operation of the ventilator.

Tom Zajdel, a post doctoral researcher, was part of the team that designed and built a new ventilator at Princeton. The open-source design uses readily available parts.

The development of the ventilator began as part of an effort by Cohen and Notterman to design a new system that was inexpensive and could be assembled from off-the-shelf parts.

It basically goes together like Legos, said Julienne LaChance, a graduate student in Cohens lab who led the prototype construction efforts from her garage. I picture my high school robotics team putting this together.

The ventilator is now fully built and meets key FDA performance standards, while costing less than $1,500 a tenth or twentieth the price of commercial ventilators, Cohen said. The team is now actively seeking manufacturing partners to help push for regulatory approval, especially in less affluent countries in need of ventilators.

We have been using robust, simple parts that we put together with a lot of very well done software and coding, said Cohen. We are trying to develop a generalized platform that anyone can work with, or improve upon, anywhere in the world, even after the pandemic.

Researchers from Hazans lab include senior Paula Gradu; graduate studentsXinyi Chen, Udaya Ghai, Edgar Minasyan,Karan SinghandDaniel Suo; and recent Ph.D. graduatesNaman AgarwalandCyril Zhang. In addition to LaChance, Notterman and Cohen, the local Princeton ventilator team includes postdoctoral researchersTom ZajdelandManuel Schottdorf, senior research software engineer Grant Wallace, and graduate studentsSophie DvaliandZhenyu Song, as well as a number of external collaborators.

Editors note: For the full version of this story, visitthe Engineering website.

Read the original here:
Engineering and artificial intelligence combine to safeguard COVID-19 patients - Princeton University