Philips on AWS: Accelerating AI Innovation for Healthcare
Philips uses AWS to accelerate AI solution development and reduce model training time, delivering faster innovation and improved patient outcomes. Collaborated with AWS to upskill over 5,000 employees through AWS Skill Builder for AI capabilities. Leveraged Amazon SageMaker AI ToolSuite to speed up ML development from weeks to days and integrated AI across healthcare workflows. Implemented data privacy automation and portable medical imaging solutions on AWS cloud, significantly improving operational efficiency and reducing MRI scan time. Deployed medical image analysis with AWS IoT and EKS enabling real-time processing and cost reductions.
- Organization
- Philips
- Industry
- Healthcare
- Location
- Netherlands
- Published
- April 2026
Reported outcomes
30x
timeTime & speed
Strategic outcomes
Catalog median for time & speed deployments: −55% across 674 reported metrics. Compare benchmarks →
Primary read
Use case focus
Showing 3 of 5
- 1AI Upskilling
- 2ML Model Development Acceleration
- 3Medical Imaging AI
- Accelerate delivery of AI-powered healthcare solutions while ensuring compliance and improving patient care quality.
- Overcome slow model training cycles and healthcare data integration challenges.
- Implement secure, scalable AI infrastructure for a global health technology company.
- Used AWS Skill Builder to upskill Philips' workforce in AI and ML skills organization-wide.
- Deployed AI ToolSuite on Amazon SageMaker to develop and operationalize machine learning models faster.
- Automated healthcare system integration to reduce implementation time dramatically.
- Utilized AWS cloud computing, IoT, and EKS to deliver scalable, real-time medical imaging solutions and privacy automation.
- Reduced AI solution delivery time by 30-70%.
- Cut MRI scan reconstruction time by a factor of 10, enabling quicker patient diagnoses.
- Achieved a 67% reduction in medical data privacy processing time with automated de-identification.
- Improved operational efficiency and enabled remote medical imaging capabilities.
- Significantly lowered infrastructure costs by up to 30 times for image analysis workloads.
Sources & evidence1
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