Beta Coming Q2 2026

Synthetic Medical Imaging for Clinical AI

Built for clinical AI startups and researchers. Generate physics-based medical images with perfect ground truth — no IRB approvals or data agreements required.

generate_mri.py
# Configure MRI parameters
import requests
# Set imaging parameters
params = {
"modality": "T1-weighted",
"anatomy": "brain",
"slice_thickness": 1.0,
"resolution": 512
}
# Generate synthetic MRI
res = requests.post(
"https://api.medsim.dev/v1/mri",
json=params
)

Sample Output

Synthetic MRI Brain Scan 1
Synthetic MRI Brain Scan 2

The Problem

Medical AI Development Is Stuck on Data

Current medical AI development is constrained by limited access to high-quality clinical imaging data, especially for validation where datasets must be independent, unbiased, and acceptable to regulators.

$50k–$500k per FDA Submission

Medical AI startups spend hundreds of thousands acquiring and annotating data for a single regulatory submission.

Months of Data Collection

Obtaining high-quality, annotated clinical imaging data is slow, requiring IRB approvals and data use agreements.

Privacy & Compliance Barriers

Medical data is highly sensitive, requiring HIPAA and GDPR compliance, making it difficult to access and use.

Rare Pathologies Are Scarce

Edge cases and rare conditions are underrepresented in clinical datasets, limiting model robustness.

Why Physics-First

The Physics-First Difference

Unlike generative AI, every synthetic image is deterministically rendered from anatomical and optical models. No mode collapse. No artifacts. Full traceability.

Anatomy Anchored

Parameters define disease state and vascular structure before image formation. Every pixel is traceable to a physiological model.

Physics Simulation

Ray-tracing and fluid dynamics simulate the actual imaging device optics. No shortcuts, no hallucinations.

Regulatory Ready

Traceable ground truth accepted as evidence for FDA MDDT pathways. Built for the scrutiny of clinical deployment.

How It Works

One API Call. Clinically Accurate Output.

Choose your imaging modality, send anatomy parameters, and receive publication-ready synthetic images with complete ground-truth maps.

Send anatomy parameters, receive publication-ready synthetic fundus images with complete ground-truth segmentation maps.

POST /v1/fundus
# Python SDK
from medsim import Client

client = Client("sk_live_...")

result = client.fundus.generate(
  pathology="diabetic_retinopathy",
  severity="moderate",
  resolution=2048,
  demographics={
    "age": 62,
    "ethnicity": "south_asian"
  }
)
200 OK
Response
{
  "id": "img_8f3k2m9x",
  "status": "complete",
  "image_url": "https://cdn...",
  "ground_truth": {
    "vessel_map": "https://cdn...",
    "lesion_mask": "https://cdn..."
  },
  "render_time_ms": 1247
}
Generated Output Preview
Synthetically generated retinal fundus image showing detailed vasculature and optic disc

Use Cases

Built for Clinical AI Startups & Researchers

From academic research through clinical deployment and regulatory submission, MedSim API integrates into your existing workflows.

Accelerate Medical Research Without Data Bottlenecks

Whether you're studying Alzheimer's biomarkers in retinal imaging or brain MRI, characterizing rare pathologies across modalities, or analyzing retinal layer changes in OCT — MedSim API gives researchers instant access to controlled, labeled synthetic data. No IRB approvals. No months-long data agreements.

  • Generate disease-specific datasets for any research hypothesis
  • Study progression across severity levels with controlled parameters
  • Perfect ground truth for quantitative analysis and paper-ready figures
  • Academic pricing available for .edu-affiliated researchers

Validation

Experimentally Validated

We've proven that our physics-first approach produces synthetic data suitable for AI training, validation, and regulatory submission.

Proven Performance

Our synthetic fundus images trained a vessel segmentation model that achieved performance parity with real-world data on DRIVE and STARE clinical datasets.

Zero Labeling Cost

Perfect ground truth segmentations, lesion boundaries, and quantitative biomarkers are generated automatically—no manual annotation required.

Scalable & Reproducible

Generate thousands of high-fidelity images with deterministic, version-controlled parameters for reproducible benchmarks.

Pricing

Simple, Transparent Pricing

Pay per image with no upfront commitment. Academic researchers with a .edu email get a 40% discount — because breakthroughs shouldn't be gated by budget.

.edu Discount
Researcher

For academic researchers and students with a .edu email. Full API access at a discounted rate.

$0.03per image

~$3 per 100 images

  • All imaging modalities (Fundus, MRI, OCT)
  • Full ground-truth segmentation maps
  • Up to 10,000 images / month
  • Python SDK & REST API access
  • Priority email support
  • Dataset export in standard formats
Startup

For clinical AI startups building and validating models for production deployment.

$0.05per image

~$5 per 100 images

  • All imaging modalities (Fundus, MRI, OCT)
  • Full ground-truth segmentation maps
  • Up to 100,000 images / month
  • Python SDK & REST API access
  • Priority support with SLA
  • Batch generation & webhooks
  • Deterministic versioned datasets
Enterprise

For organizations needing custom modalities, volume pricing, and regulatory support.

Customvolume pricing

Tailored to your needs

  • Everything in Startup, plus:
  • Unlimited image generation
  • Custom modality development
  • On-premise deployment option
  • Dedicated account manager
  • Regulatory documentation support
  • Custom SLA & compliance review

Need high-volume batch generation?

All plans support batch API calls. Generate up to 1,000 images per request with automatic queuing and webhook notifications. Volume discounts available for batches over 50,000 images.

FAQ

Frequently Asked Questions