Image generation for life sciences

Carez AI is a synthetic data platform for medical imaging.We help AI teams generate high-fidelity training data at scale — reducing bottlenecks and accelerating model development.

Let's check if you're a fit

Faster R&D Cycles

Generate datasets on demand — no waiting for hospital timelines.

Ready-to-Use Synthetic Data

Use data that’s fully synthetic — no patient recruitment, no clinical delays.

Access Rare & Edge Cases

Create the cases you can’t collect — instantly.

Instant Dataset Diversity

Balance demographics without oversampling or bias.

Train Without Privacy Risk

No PHI, no legal hold-ups — just clean, usable data.

Built for Model Validation

Drop into your AI pipeline to test, fine-tune, and stress models early.

Generate. Validate. Repeat.

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Train
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Generate
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Validate
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Export

Launch your first synthetic dataset

Technical walkthrough with our founding team

Built for synthetic performance

Generate thousands of synthetic examples with regulatory control, demographic balance, and real-world quality — instantly.

As featured in
Carez AI selected by Google for Startups global accelerator programCarez AI featured in Tech:NYC as an emerging healthcare AI companyCarez AI backed by Creative Destruction Lab for scientific deep tech accelerationCarez AI selected for ERA NYC cohort, 1% acceptance rate startup acceleratorCarez AI research presented at CIRSE — Society for Interventional Radiology in Europe


Access 100 free synthetic images — high-fidelity, regulator-ready

Trusted by top AI teams in life sciences

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Frequently Asked
(but never boring) Questions
What’s synthetic data — and why not just use real data? +
Real data is often messy, biased, or hard to access. Synthetic data lets us create clean, customizable, privacy-safe datasets — faster, cheaper, and fully compliant.
Is this FDA compliant or just hype? +
Our platform is being tested in partnership with hospital systems and regulators. We’re building for actual adoption — not academic novelty.
Can I generate rare cases or edge conditions? +
Absolutely. Our system lets you target underrepresented demographics, modalities, and disease states — the stuff your model probably struggles with.
Who uses this? +
AI teams at life sciences startups, imaging companies, and R&D labs — anyone trying to build smarter, fairer models with limited access to high-quality data.

Read Our Blog

Intro to Synthetic Data
How We Built Our First Dataset
Validating with Real-World Cases
Working with Radiologists
Why Privacy Still Matters
Balancing Data by Design
Improving Model Generalization
Lessons from Our FDA Feedback