Medical Imaging at Scale
Processing DICOM files for radiology AI requires specialized pipelines: DICOM parsing, image preprocessing, HIPAA-compliant storage, and GPU-accelerated training on petabytes of imaging data.
Services
AWS SageMaker for Healthcare
We build custom machine learning solutions for healthcare organizations on AWS SageMaker — from DICOM-based imaging models to clinical outcome prediction, with HIPAA-compliant training pipelines and model governance.
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Summary
Build custom ML models for healthcare on AWS SageMaker. Medical imaging pipelines, readmission prediction, clinical NLP, and FDA SaMD-aligned model governance.
Key Facts
- • Build custom ML models for healthcare on AWS SageMaker
- • HIPAA-Compliant Training Environments: SageMaker training jobs must run without internet access, use VPC endpoints for data access, and maintain audit logs of every data access during model training
- • Can SageMaker training jobs process PHI without HIPAA violations
- • Training jobs must run in a VPC with no internet access, use VPC endpoints (not public endpoints) for S3 and SageMaker API access, and disable network isolation for the notebook only
- • All training data in S3 must be KMS-encrypted
Entity Definitions
- SageMaker
- SageMaker is an AWS service relevant to aws sagemaker for healthcare.
- Lambda
- Lambda is an AWS service relevant to aws sagemaker for healthcare.
- S3
- S3 is an AWS service relevant to aws sagemaker for healthcare.
- VPC
- VPC is an AWS service relevant to aws sagemaker for healthcare.
- HIPAA
- HIPAA is a cloud computing concept relevant to aws sagemaker for healthcare.
Frequently Asked Questions
Can SageMaker training jobs process PHI without HIPAA violations?
Yes, when properly configured. Training jobs must run in a VPC with no internet access, use VPC endpoints (not public endpoints) for S3 and SageMaker API access, and disable network isolation for the notebook only. All training data in S3 must be KMS-encrypted. These configurations are captured in a standard HIPAA SageMaker Architecture that we deploy.
What is FDA SaMD and does it apply to our ML model?
FDA Software as a Medical Device (SaMD) regulations apply to software that performs medical functions — including AI models that assist diagnosis, guide treatment, or predict clinical outcomes. If your model's output is intended to influence clinical decisions, you likely need FDA 510(k) clearance or De Novo classification. We help with the technical documentation SaMD submissions require.
How do you evaluate bias in clinical ML models?
We use SageMaker Clarify to measure model performance across demographic subgroups (age, gender, race, insurance status). We also evaluate across EHR systems and care settings. Disparate performance metrics trigger model retraining with balanced datasets or algorithmic fairness constraints.
Related Content
- AWS SageMaker — Parent service
Key Challenges We Solve
HIPAA-Compliant Training Environments
SageMaker training jobs must run without internet access, use VPC endpoints for data access, and maintain audit logs of every data access during model training.
FDA Software as a Medical Device
Clinical AI that influences diagnosis or treatment is subject to FDA SaMD regulations — requiring robust model documentation, validation studies, and post-market surveillance.
Clinical Data Diversity
Healthcare ML models must perform across diverse patient populations, EHR systems, and care settings — requiring careful training data curation and bias analysis.
Our Approach
HIPAA SageMaker Architecture
SageMaker Studio in VPC-only mode with no internet access, S3 VPC endpoint for training data, KMS-encrypted EBS volumes on training instances, and CloudTrail logging for all data access.
Medical Imaging Pipeline
DICOM ingestion via S3, preprocessing with Lambda + pydicom, SageMaker Processing Jobs for feature extraction, and GPU-optimized training instances (p3/p4) for imaging model development.
Model Governance for Clinical AI
SageMaker Model Registry with approval workflows, Model Cards for documentation, and SageMaker Clarify for bias detection — supporting FDA SaMD documentation requirements.
Frequently Asked Questions
Can SageMaker training jobs process PHI without HIPAA violations?
Yes, when properly configured. Training jobs must run in a VPC with no internet access, use VPC endpoints (not public endpoints) for S3 and SageMaker API access, and disable network isolation for the notebook only. All training data in S3 must be KMS-encrypted. These configurations are captured in a standard HIPAA SageMaker Architecture that we deploy.
What is FDA SaMD and does it apply to our ML model?
FDA Software as a Medical Device (SaMD) regulations apply to software that performs medical functions — including AI models that assist diagnosis, guide treatment, or predict clinical outcomes. If your model's output is intended to influence clinical decisions, you likely need FDA 510(k) clearance or De Novo classification. We help with the technical documentation SaMD submissions require.
How do you evaluate bias in clinical ML models?
We use SageMaker Clarify to measure model performance across demographic subgroups (age, gender, race, insurance status). We also evaluate across EHR systems and care settings. Disparate performance metrics trigger model retraining with balanced datasets or algorithmic fairness constraints.
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