RWID Core · For AI Medical Devices and Big Techs

Large-scale real-world imaging data for foundation models, from pipeline to production to FDA.

See what’s available for your modality
Talk to our team
Millions of multi-vendor, multi-site imaging studies you can train, fine-tune, and validate across, centralized and de-identified under both HIPAA methods.
50+
FDA-cleared devices built on Segmed healthcare partners data
HIPAA
Safe Harbor + Expert Determination
2,800
Healthcare partner sites

Challenge

Pre-training is a data problem. So is everything after it.

A generalist imaging model is only as good as the breadth it trained on and it still has to survive fine-tuning, pass validation, and clear the FDA.

Every one of those stages is the same underlying question: has the model seen enough of the real world? Narrow data breaks a model at pre-training and again at the generalizability review. Segmed is built imaging-first and centralized by design: one unified, de-identified corpus you can pre-train, fine-tune, and validate across.

What You Get

One network, the whole model lifecycle
Our Foundation Model datasets are large-scale, multi-vendor imaging collections, engineered for the requirements of its stage in the model lifecycle: from first training run to FDA.

Modalities

Know what you’re training on
Foundation-model teams wanthead-to-toe breadth by modality, not a single headline number. A live snapshotof the network, by modality:

Use Cases

How does this apply to your work?
Each dataset supports four persona-aligned use cases across the drug development lifecycle.
Request datasets

Foundation-model /
ML lead (Big Tech)

Medical imaging data head-to-toe, multi-vendor breadth for pre-training generalist imaging models, centralized, so you train across the whole distribution.

AI / ML lead
(AI Medical Devices)

Targeted diversity that closes the off-site performance gap; fine-tune and validate on the same corpus.

Regulatory and
Clinical Validation

Independent, multi-sitetest cohorts with provenance and demographic breadth, structured for generalizability and lineage end to end.

Founder / VP Product

De-risk the whole roadmap: pre-training, validation, and post-market from one subscription or licensing.

Why Segmed

Built imaging-first.
Across the whole lifecycle.
A model only generalizes on the breadth it was built on. Segmed is the centralized, imaging-first network that gives you that breadth and the documentation to defend it, from the first training run to the FDA.

Imaging at Scale

Segmed

150M+ studies

Others

Limited or none

Imaging + EHR linkage

Segmed

3.5M patients linked

Others

Partial or absent

Built imaging-first

Privacy-preserving

Segmed

Yes

Segmed

Yes

Others

None

Others

Varies

The Data You Need

Proven where it counts. 50+ FDA-cleared medical devices have been built on Segmed healthcare partners data.

Proven across the model lifecycle

Powering large-scale pre-training and FDA-grade validation for leading medical-imaging AI developers across CT, MRI, X-ray, mammography, and ultrasound.

Published research

Peer-reviewed work on applications of real-world imaging data for foundational models use cases.

Access timeline

Data in 2 weeks, not months. Compliance infrastructure (de-identification, provenance documentation) managed on the platform.

Dataset Request

See what’s available for your modality
Tell us the modality, bodypart, and population you’re training or validating on. We’ll send a modalitybreakdown, available multi-site volume.

Frequented Asked Questions - F.A.Q.

Can Segmed's real-world imaging data support large-scale pre-training?

Yes, millions of studies,head-to-toe across modalities, vendors, and sites: the non-curated breadthgeneralist models need. Ask for the modality breakdown.

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Can the same network handle validation later?

That’s the point: onecentralized network across the full lifecycle: pre-train, fine-tune, validate, clear, and monitor, without re-sourcing at each stage.

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•Why centralized over federated?

You can pool andtrain/validate across the whole corpus consistently; federated networksstruggle when data must leave its source. Segmed stays privacy-preserving viaautomated de-identification.

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•How is the data de-identified?

To HIPAA, underboth Safe Harbor and Expert Determination, with documented provenance.

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•How fast?

2 weeks, not months.

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