Applying artificial intelligence to medical images can be beneficial to physicians and patients, but developing the tools to do it can be challenging. Google on Tuesday announced it’s ready to meet 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 now we’re making our imaging expertise, tools and technologies available for health care and life sciences enterprises,” Alissa Hsu Lynch, global lead of Google Cloud MedTech Strategy and Solutions, said in a statement.
Gartner Vice President and Distinguished Analyst Jeff Cribbs explained that health care providers who are looking for AI for diagnostic imaging solutions have generally been forced into one of two choices.
“They can procure software from the device manufacturer, the image repository vendor or from 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 health care-specific acceleration.”
“This Google product provides a platform for AI developers and also facilitates image exchange,” added Ginny Torno, administrative director of innovation and IT clinical, ancillary and research systems at Houston Methodist, in Houston.
“This is not unique to this market, but may provide interoperability opportunities that a smaller provider is not capable of,” she told TechNewsWorld.
Robust Components
According to Google, Medical Imaging Suite addresses some common pain points organizations face when developing AI and machine learning models. Components in the suite include:
- Cloud Healthcare API, which allows for easy and secure data exchange using an international standard for imaging, DICOMweb. The API provides a fully managed, scalable, enterprise-grade development environment, with automated DICOM de-identification. Imaging technology partners include NetApp for seamless on-prem to cloud data management, and Change Healthcare, a cloud-native enterprise imaging PACS in clinical use by radiologists.
- AI-assisted annotation tools from Nvidia and Monai 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 analytics and create training datasets with zero operational overhead.
- Use of Vertex AI to accelerate development of AI pipelines to build scalable machine learning models, with 80% fewer lines of code required for custom modeling.
- Flexible options for cloud, on-prem, or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy requirements — while providing centralized management and policy enforcement with Google Distributed Cloud, enabled by Anthos.
Full Deck of Tech
“A key differentiator for Medical Imaging Suite is that we’re offering a comprehensive suite of technologies that support the process of delivering AI from beginning to end,” Lynch told TechNewsWorld.
The suite provides everything from imaging data ingestion and storage to AI-assisted annotation tools to flexible model deployment options at 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.
Lynch added that 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,” she said.
“By having everything built around this standardized approach, we are making it easier for organizations to manage their data and make it useful.”
Image Classification Solution
The growing use of medical imaging, coupled with manpower issues, has made the field ripe for solutions based on artificial intelligence and machine learning.
“As imaging systems become faster, offer higher resolution and capabilities such as functional MRI, it is tougher for the infrastructure supporting those systems to keep up and ideally, stay ahead of what is needed,” Torno said.
“In addition, there are shortages in the radiology workforce that complicate the personnel side of the workloads,” she added.
Google Cloud aims to make health care imaging data more accessible, interoperable, and useful with its Medical Imaging Suite (Image Credit: Google)
She explained that AI can identify issues found in an image by comparing it to a learned set of images. “It can recommend a diagnosis that then just needs interpretation and confirmation,” she noted.
“It can also surface images to the top of a work queue if a potential life-threatening situation is detected in an image,” she continued. “AI can also organize workflows by reading images.”
Machine learning does for medical imaging what it did for facial recognition and image-based search. “Rather than identifying a dog, frisbee or chair in a photograph, the AI is identifying tumor boundary, bone fracture or lung lesion in a diagnostic image,” Cribbs explained.
Tool, Not Substitute
Michael Arrigo, managing partner at No World Borders, a national network of expert witnesses on health care issues, based in Newport Beach Calif., agreed that AI might help some over-worked radiologists, but only if it’s reliable.
“Data must 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.”
Torno added that many studies have been done around AI accuracy and will continue to be done.
“While there are examples of AI finding things that a human did not, or being ‘just as good’ as a human, there are also examples where AI misses something important, or isn’t quite sure what to interpret as there could be multiple issues with the patient,” she observed.
“AI should be seen as an efficiency tool to accelerate image interpretation and aid with emergent cases, but not completely replace the human element,” she said.
Big Splash Potential
With its resources, Google can make a significant impact on the medical imaging market. “Having a major player like Google in this space could facilitate synergies with other Google products already in place at health care organizations, potentially enabling more seamless connectivity to other systems,” Torno noted.
“If Google concentrates on this market segment, they have the resources to make a splash,” she continued. “There are many players in this space already. It will be interesting to see how this product can leverage other Google functionality and pipelines and be a differentiator.”
Lynch explained that with the launch of 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 ease the burden for health care workers and significantly improve and even save people’s lives,” she said.
“By offering our imaging tools, products and expertise to health care organizations, we believe the market and patients will benefit,” she added.
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