The partnership is aimed at supporting healthcare providers’ transition to a decentralised model that is virtual and distributed
GE Healthcare has partnered with Amazon Web Services (AWS) to offer AI and cloud-based imaging solutions to hospitals and healthcare providers.
The partnership is aimed at supporting healthcare providers’ transition from the traditional care delivery model to a decentralised model that is virtual and distributed.
With the partnership, GE Healthcare will make available its AI-based imaging applications and Edison Health Services platform on AWS.
According to the company, Edison Health Services platform enables clinicians to derive and manage insights from the 3.6 billion imaging procedures and 50 petabytes of data produced by hospitals annually.
GE Healthcare chief digital officer Amit Phadnis said: “As the world moves towards a more virtualized and distributed care delivery model with home care, remote patient management, and increased use of AI, radiologists and other clinicians need easy access to data that is seamlessly integrated, aggregated, and visualised in applications and services across modalities and within their existing workflows.
“By doing this at scale, we are helping to drive clinical outcomes and achieving our goals of transforming healthcare to be more efficient and personalised.”
Under the partnership, GE Healthcare’s Edison True PACS, Picture Archive and Communication System (PACS) will be the first AI-enabled imaging solution that will be available on the AWS.
The medical imaging storage system is expected to allow hospitals to improve access to care and deliver clinical insights.
Edison True PACS is a diagnostic imaging and workflow solution that is designed to enable radiologists to adapt to higher workloads and improve diagnostic accuracy.
Currently, the solution is available in the US and is expected to be launched in some other regions starting in 2022.
GE Healthcare will also offer its Edison data aggregation and AI analytics platform on AWS to allow developers to build, test, and validate new AI models.