MAEA
Build and deploy medical AI in one workflow.
Prepare data, annotate faster, train models, validate performance, and deploy with healthcare-ready traceability.
MAEA is ModAstera's end-to-end platform for medical AI teams. It brings together annotation, experimentation, evaluation, deployment, and documentation so teams stop rebuilding the pipeline around every project.
Why teams switch from fragmented workflows
Medical AI teams often juggle separate annotation tools, experiment infrastructure, deployment handoffs, and compliance documents. MAEA brings those stages into one operating surface so projects move with fewer delays and fewer specialist bottlenecks.
Fragmented workflow | MAEA workflow |
|---|---|
Annotation, training, and deployment live in separate tools. | One platform connects dataset preparation, model work, and deployment. |
Teams lose time to manual experiment setup and repeated handoffs. | Experiments, validation, and iteration happen in one continuous flow. |
Operational and compliance records are assembled after the fact. | Traceability and documentation stay close to the work as it happens. |
Domain experts depend heavily on scarce ML infrastructure support. | Researchers, clinicians, and technical teams can collaborate in the same workspace. |
Annotation, training, and deployment live in separate tools.
Teams lose time to manual experiment setup and repeated handoffs.
Operational and compliance records are assembled after the fact.
Domain experts depend heavily on scarce ML infrastructure support.
Annotation
Start with training-ready data
High-quality medical AI starts with high-quality data preparation. MAEA includes AI-assisted annotation workflows so teams can create cleaner datasets without turning labeling into the whole project.
- Faster annotation with assistive labeling and review loops
- Cleaner datasets with lineage, version history, and auditability
- A smoother handoff from dataset preparation into training and deployment
Annotation capabilities inside MAEA
Few-shot assisted labeling
Start with a small set of examples and expand to larger datasets with AI-generated draft labels.
Active learning and review
Focus expert attention on uncertain cases, approve corrections quickly, and improve the next batch.
Clinical-grade quality control
Support review flows, dataset versioning, and traceability that help teams prepare for downstream evaluation and compliance work.
How the annotation stage works
01
Upload and frame the task
Bring in imaging or clinical datasets and define the labeling objective for your project.
02
Review AI-assisted drafts
Experts correct boundaries, approve suggestions, and focus on the cases that need human judgment.
03
Move directly into training
Use the prepared dataset in the same product flow to train, evaluate, and deploy models.
Supported data and modalities
Choose Your Plan
Select the perfect plan for your healthcare AI development needs
Best for personal evaluation and early exploration
Ideal for personal evaluation and early exploration
Manage datasets and annotate with AI-assist
Train 2 predictive models
Storage up to 100 MB
Deploy 1 model with capped inference compute in the platform interface
Perfect for researchers and small teams
Ideal for researchers and small teams, up to 3 users
Manage datasets and annotate with unlimited AI-assist
Train unlimited predictive models
Storage up to 10 GB
Deploy models in one-click with API access
Perfect for growing teams
Everything in Team
Collaborative workspace with teammates
Train GPU-based models
Organization level administration
For large-scale deployments
Custom setup
Unlimited everything
Dedicated support
Custom integrations
SLA guarantees