Applying artificial intelligence to medical images can be beneficial to clinicians and patients, but developing the tools to do so can be challenging. Google announced on Tuesday that it is ready to take on that challenge with its new medical imaging suite.

“Google pioneered the use of AI and computer vision in Google Photos, Google Image Search, and Google Lens, and we are now making our imaging expertise, tools and technology available to healthcare and life science enterprises,” said Alisa Sou. Lynch, global lead of Google Cloud MedTech Strategy and Solutions, said in a statement.

Jeff Cribbs, Gartner’s vice president and distinguished analyst, explained that health care providers who are looking to AI for diagnostic imaging solutions are typically forced into one of two choices.

“They can purchase software from a device manufacturer, image store vendor or a third party, or they can build their own algorithms with industry agnostic image classification tools,” he told TechNewsWorld.

“With this release,” he continued, “Google is taking their low-code AI development tooling and adding substantial healthcare-specific acceleration.”

“This Google product provides a platform for AI developers and also facilitates image exchange,” said Ginny Torno, administrative director of innovation and IT clinical, assistant and research systems at Houston Methodist in Houston.

“It is not unique to this market, but can provide opportunities for interoperability that a smaller provider is not capable of,” she told TechNewsWorld.

strong component

According to Google, the medical imaging suite addresses some common pain points when developing AI and machine learning models. Components in the suite include:

• Cloud Healthcare API, which allows easy and secure data exchange using DICOMweb, an international standard for imaging. API provides a fully managed, scalable, enterprise-grade development environment with automated DICOM de-detection. Imaging technology partners include NetApp for seamless on-premises cloud data management and cloud-native enterprise imaging PACS Change Healthcare in clinical use by radiologists.
• AI-assisted annotation tools from Nvidia and Monae to automate the highly manual and repetitive task of labeling medical images, as well as native integration with any DICOMWeb viewer.
• Access to BigQuery and Looker to view and search petabytes of imaging data to perform advanced analysis and create training datasets with zero operational overhead.
• Using Vertex AI to accelerate the development of AI pipelines to build scalable machine learning models with up to 80% fewer lines of code required for custom modeling.
• Flexible options for cloud, on-premises, or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy needs – while providing centralized management and policy enforcement with Google Distributed Cloud, enabled by Anthos.

full deck of tech

“One key difference to the medical imaging suite is that we are offering a comprehensive suite of technologies that support the process of delivering AI from start to finish,” Lynch told TechNewsWorld.

The suite offers everything from imaging data ingestion and storage to AI-assisted annotation tools to flexible model deployment options on the edge or in the cloud, she explained.

“We are providing solutions that will make this process easier and more efficient for health care organizations,” she said.

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Lynch said the suite takes an open, standardized approach to medical imaging.

“Our integrated Google Cloud services work with a DICOM-standard approach, allowing customers to seamlessly leverage Vertex AI for machine learning and BigQuery for data discovery and analytics,” he added.

“By building everything around this standardized approach, we’re making it easier for organizations to manage their data and make it useful.”

image classification solution

The increasing use of medical imaging, coupled with manpower issues, has made the field ready for solutions based on artificial intelligence and machine learning.

Torno said, “As imaging systems get faster, offering higher resolution and capabilities like functional MRI, it is harder for the infrastructure to maintain those systems and, ideally, stay ahead of what is needed.” “

“In addition, there is a reduction in the radiology workforce that complicates the personnel side of the workload,” she said.

Google Cloud aims to make health care imaging data more accessible, interoperable and useful with its medical imaging suite (Image Credit: Google)

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She explained that AI can identify issues found in an image from a learned set of images. “It may recommend a diagnosis that then only needs interpretation and confirmation,” she said.

“If the image detects a potentially life-threatening situation, it can also project the images to the top of a task queue,” she continued. “AI can also streamline workflows by reading images.”

Machine learning does for medical imaging what it did for facial recognition and image-based search. “Instead of identifying a dog, Frisbee or chair in a photograph, AI is identifying the extent of a tumor, bone fracture or lung lesion in a diagnostic image,” Cribbs explained.

tools, not substitutes

Michael Arrigo, managing partner of No World Borders, a national network of expert witnesses on health care issues in Newport Beach, Calif., agreed that AI could help some overworked radiologists, but only if it be reliable.

“Data should be structured in ways that are usable and consumable by AI,” he told TechNewsWorld. “AI doesn’t work well with highly variable unstructured data in unpredictable formats.”

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Torno said that many studies around AI accuracy have been done and will be done further.

“While there are examples of AI being ‘just as good’ as a human didn’t have, or being ‘just as good’ as a human being, there are also examples where an AI misses something important, or isn’t sure.” That’s what to interpret because there may be many problems with the patient,” she observed.

“AI should be seen as an efficiency tool to accelerate image interpretation and assist in emergent cases, but should not completely replace the human element,” she said.

large splash capacity

With its resources, Google can have a significant impact on the medical imaging market. “Having a major player like Google in this area could facilitate synergy with other Google products already in place in healthcare organizations, potentially enabling more seamless connectivity to other systems,” Torno said.

“If Google focuses on this market segment, they have the resources to make a splash,” she continued. “There are already many players in this area. It will be interesting to see how this product can take advantage of other Google functionality and pipelines and become a differentiator.”

Lynch pointed out that with the launch of the medical imaging suite, Google hopes to help accelerate the development and adoption of AI for imaging by the health care industry.

“AI has the potential to help reduce the burden for health care workers and improve and even save people’s lives,” she said.

“By offering our imaging tools, products and expertise to healthcare organizations, we are confident that the market and patients will benefit,” he added.