Google Cloud has announced Medical Imaging Suite, a new industry solution that makes imaging health care data more accessible, interoperable and useful.
Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90 percent of all health care data and, until now, these complex images have been highly dependent on humans to read. In addition, the number of images continues to grow, increasing the workload for radiologists and other health care professionals tasked with interpreting these images for clinicians and patients. Google Cloud enables the development of AI for imaging to support faster, more accurate diagnosis of images, increased productivity for health care workers and improved care access and outcomes for patients.
“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,” said Alissa Hsu Lynch, Global Lead of Google Cloud’s MedTech Strategy and Solutions. “Our Medical Imaging Suite shows what’s possible when tech and health care companies come together.”
Google Cloud’s Medical Imaging Suite addresses common pain points organizations face in developing AI and machine learning models and uses this to enable data interoperability. Components of the Medical Imaging Suite include:
- Imaging Storage: Cloud Healthcare API, part of the Medical Imaging Suite, allows easy and secure data exchange using the international DICOMweb standard for imaging. Cloud Healthcare API provides a fully managed, highly scalable, enterprise-grade development environment and includes 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.
- Imaging Lab: AI-assisted annotation tools from NVIDIA and MONAI help automate the highly manual and repetitive task of labeling medical images, and Google Cloud also offers native integration with any DICOMweb viewer.
- Imaging Datasets & Dashboards: Organizations can use BigQuery and Looker to view and search petabytes of imaging data to perform advanced analytics and create training datasets with zero operational overhead.
- Imaging AI Pipelines: Using Vertex AI on Google Cloud can accelerate development of AI pipelines to build scalable machine learning models, with 80 percent fewer lines of code required for custom modeling.
- Imaging Deployment: Finally, the Medical Imaging Suite offers 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.
