Google Cloud Introduces New Al-Powered Medical Imaging Suite
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Using artificial intelligence to analyze medical images can benefit both doctors and patients, but
improving the tools to do so can be difficult. Google stated on Tuesday that it is prepared to tackle that
dilemma with its new Medical Imaging Suite.
According to Alissa Hsu Lynch, global lead of Google Cloud MedTech Strategy and Solutions, Google
initiated the use of AI and computer vision in Google Photos, Google Image Search, and Google Lens,
and now we’re creating our imaging expertise, tools, and systems accessible to healthcare care and life
sciences businesses.
According to Jeff Cribbs, Vice President and Distinguished Analyst at Gartner, health care providers
seeking for AI for diagnostic imaging solutions are mainly limited to one of two options.
He told TechNewsWorld that they could buy software from the device manufacturer, the image
repository vendor, or a third party, or they could create their own algorithms using industry-neutral
image classification tools.
He went on to say that Google is holding their low code AI development tooling and enhancing it with
significant health care-specific acceleration.
Ginny Torno, administrative director at Houston Methodist, included that this Google product arranges
a stage for Al developers and eases image exchange.
She stated that this is not exceptional to this sector, but it may offer additional opportunities for
connectivity that a fairly small provider cannot.
Robust Components
According to Google, Medical Imaging Suite outlines some of the most common issues that businesses
face when developing machine learning and AI models. The suite’s aspects entail:
Cloud Healthcare API, which enables simple and secure data exchange via DICOMweb, an international
imaging standard. The API offers a fully maintained, customizable, entrepreneurial development
environment with DICOM de-identification automation. NetApp for seamless on-premises to cloud data
management and Change Healthcare, a cloud-native enterprise imaging PACS in medical use by
radiologists, are two imaging technology partners.
Nvidia and Monai AI-assisted annotation tools to simplify the standardized work and dull task of
labelling medical images, along with native integration with any DICOMweb viewer.
BigQuery and Looker access to outlook and browse petabytes of imaging data for advanced analytics
and training datasets with no operational overhead.
Vertex AI is used to accelerate the development of AI pipelines for building scalable machine learning
models, requiring 80% fewer lines of code for custom modelling.
Google Distributed Cloud, empowered by Anthos, provides organizations with advanced features for
cloud, on-premises, or edge deployment to satisfy different sovereignty, digital security, and data
protection requirements — all while providing centralized control and policy enforcement.
Full Deck of Tech
Lynch stated that one of the key differentiators for Medical Imaging Suite is that it provides a complete
suite of technologies that aid the method of providing AI from start to finish.
According to her, the suite includes anything from imaging data consumption and storage to AI-assisted
annotation methods and adaptable model deployment choices at the edge or in the cloud.
“We are offering solutions to make this operation simpler and more effective for health care
organizations,” she explained.
Lynch did add that the suite approaches medical imaging in an open, standardized manner.
Our integrated Google Cloud services, she explained, use a DICOM-standard framework, allowing people
to effortlessly control Vertex AI for machine learning and BigQuery for large datasets and predictive
analysis.
Image Classification Solution
The increasing use of medical imaging, combined with labor shortages, has created an ideal
environment for systems that rely on AI and machine learning.
Torno stated that as imaging systems improve in speed, resolution, and functionality such as functional
MRI, it becomes more difficult for the facilities assisting those technologies to keep up and, presumably,
remain ahead of what is required.
“There are also shortfalls in the radiology workforce, which complicates the workforce side of the
workloads,” she placed.
She made clear that AI can detect problems in images by making comparisons them to a prior
knowledge set of images. “It can strongly suggest an assessment that then just demands to be
interpreted and confirmed,” she explained.
Tool, Not Substitute
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Big Splash Potential
Google is capable of leaving a remarkable effect on the medical imaging market with its resources.
Torno highlighted that the presence of a major player in this space, such as Google, could support
synergy effects with other Google products already in place at health care organizations, potentially
allowing for more interconnection to other systems.
She says, they hold the resources that are necessary to ace only if Google focuses on the market
segment. It will be exciting to witness the resistance of this product towards other Google functions and
stand against the many competitors in this sector.
Lynch stated that by releasing Medical Imaging Suite, Google intends to speed up the creation and
surrogacy of AI for imaging by the health care sector.
She also believes that AI has the capability to reduce the burden on health-care workers while also
significantly improving and even saving people’s lives.
We believe that by providing our imaging tools, products, and expertise to health care organizations, the
market and patients will stand to gain, she incorporated.
Source Link: Google Cloud Introduces New AI-Powered Medical Imaging Suite (technewsworld.com)
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The post Google Cloud Introduces New Al-Powered Medical Imaging Suite appeared first on Kore Digital.
