OneMedNet Receives Mid Six-Figure Re-Order from Foundation Model Company Developing Radiology Models
Key Highlights
- ➤OneMedNet (ONMD) receives mid six-figure re-order from radiology foundation-model company
- ➤Re-order expands multi-modal imaging data agreement included in $3 million-plus June bookings
- ➤Full-fidelity DICOM studies preserve pixel data and acquisition metadata for model training
- ➤OneMedNet data network spans more than 2,300 healthcare partner sites
Expert Statements
Aaron Green, President & CEO of OneMedNet
“We have seen customers come back for repeat orders because of the speed with which we deliver, the quality of our full-fidelity data, and the fact that it comes directly from our healthcare provider partners — not intermediaries or secondary markets.”
Aaron Green, President & CEO of OneMedNet
“Nowhere does that matter more than in foundation AI. You cannot train a AI radiology model on degraded images or incomplete studies. We believe this order reflects a customer that has worked with our data at scale and determined it is the right foundation for the next stage of its model development.”
MINNEAPOLIS, Nov. 04, 2026 (GLOBE NEWSWIRE) -- OneMedNet Corporation (Nasdaq: ONMD) (“OneMedNet,” the “Company,” “we,” “us” or “our”), a leading provider of First-Party (Direct-from-Source) regulatory decision-grade, AI-driven Real-World Data (RWD), today announced that it has received a mid six-figure re-order from a previously announced foundation model company developing advanced AI models for radiology. This order follows OneMedNet’s delivery of the first dataset under the customer agreement — based on the quality and completeness of that data. The customer is expanding on the multi-modal imaging data agreement, which was included as part of the more than $3 million in new bookings the Company announced on June 10, 2026, including engagements supporting foundation model development.
Under the agreement, OneMedNet delivers de-identified, multi-modal studies at scale, sourced directly from its healthcare provider partners. The re-order increases that data volume as the customer advances its model development. AI development in foundation radiology modeling depends on full-fidelity DICOM studies and images — complete imaging series with intact pixel data and acquisition metadata — delivered at scale. OneMedNet de-identifies each study at the source while preserving the diagnostic fidelity these models require, a standard that is difficult to meet with compressed or stripped-down secondary data.
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