What challenges did GE Healthcare face before AI implementation?

GE Healthcare faced the challenge of improving collaboration and access to medical imaging data to reduce misdiagnosis and ultimately improve patient outcomes. Clinicians needed faster and easier access to imaging data from disparate systems and locations.

What was the impact of the AI solution?

The solution improved data accessibility and interoperability, potentially reducing costly misdiagnoses and saving healthcare systems billions. Faster access to imaging data has enhanced clinician collaboration and patient care. Machine learning enhancements through Amazon SageMaker improved diagnostic capabilities.

Rank#2,920of 3,449

Source

ExpandedEvidence: Low40/100

GE Healthcare Launches Health Cloud on AWS to Improve Collaboration and Patient Outcomes

Use case typePricing optimizationUpdated Jun 13, 2026

GE Healthcare developed the GE Health Cloud on AWS to improve collaboration and access to medical imaging data, enabling faster and more secure sharing among clinicians globally. The solution collects, stores, and processes medical imaging data from devices worldwide, enhancing interoperability and data accessibility across hospitals and health systems. AWS services used include Amazon SageMaker for machine learning and deep learning capabilities, Amazon Simple Storage Service (S3) for storage, Amazon Elastic Compute Cloud (EC2) for compute infrastructure, AWS Service Catalog for application deployment, and Amazon Cognito for user authentication and security.

Organization: GE Healthcare
Industry: Healthcare
Location: United States
Published: May 2023

Reported outcomes

Strategic outcomes

New product / capabilityLaunched a cloud-based imaging data platformCustomer experience & trustImproved clinician collaboration and patient careNew product / capabilityEnhanced diagnostic capabilities with machine learningCustomer experience & trustEnabled secure sharing of medical imaging data

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: GE Healthcare
Provider: AWS
Maturity: Unknown
Linked source: AWS Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

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1Machine Learning

GE Healthcare faced the challenge of improving collaboration and access to medical imaging data to reduce misdiagnosis and ultimately improve patient outcomes.
Clinicians needed faster and easier access to imaging data from disparate systems and locations.

The GE Health Cloud was built on AWS to securely aggregate imaging data from medical devices around the world, providing clinicians with rapid access to interoperable and consolidated data.
Machine learning models were enhanced with Amazon SageMaker to analyze imaging data and assist in diagnostics.
The solution architecture incorporated AWS Service Catalog and Amazon Cognito to ensure scalable and secure deployment and user access management.

Technologies

Amazon SageMaker Amazon Simple Storage Service Amazon Elastic Compute Cloud AWS Service Catalog Amazon Cognito

The solution improved data accessibility and interoperability, potentially reducing costly misdiagnoses and saving healthcare systems billions.
Faster access to imaging data has enhanced clinician collaboration and patient care.
Machine learning enhancements through Amazon SageMaker improved diagnostic capabilities.

Architecture

The architecture includes data ingestion from global medical imaging devices into a secure AWS cloud environment using Amazon S3 for storage and Amazon EC2 for compute resources. Amazon SageMaker runs ML and deep learning workloads for imaging analysis. AWS Service Catalog manages application deployment, and Amazon Cognito secures user access.

Sources & evidence1

Evidence: Low40/100Evidence strength