Key Highlights

• OneMedNet received a mid six-figure re-order from a foundation model company developing advanced AI models for radiology.

• The repeat order follows successful delivery of the first dataset under a previously announced multi-modal imaging data agreement.

• OneMedNet delivers full-fidelity DICOM studies with intact pixel data and Acquisition metadata, sourced from over 2,300 healthcare partner sites.

• The company's data spans diverse patient demographics, anatomical regions, scanner manufacturers, and acquisition protocols.

OneMedNet Corporation(NASDAQ:ONMD), a provider of regulatory decision-grade, AI-driven real-world data, announced a mid six-figure re-order from a previously disclosed foundation model company that is developing advanced artificial intelligence models for radiology. The order underscores the growing Demand for high-quality medical imaging data to train increasingly sophisticated AI systems.

Repeat Business Validates Data Quality

The re-order came after OneMedNet delivered the initial dataset under an existing customer agreement, which was part of more than $3 million in new bookings the company announced on June 10, 2026. The customer chose to expand the multi-modal imaging data agreement based on the quality and completeness of the first delivery. The expanded order increases the Volume of data to be provided as the customer advances its model development.

President and CEO Aaron Green noted that repeat orders have been driven by the speed of delivery, the quality of full-fidelity data, and the fact that it is sourced directly from healthcare providers rather than intermediaries or secondary markets. He emphasized that foundation AI in radiology cannot be effectively trained on degraded images or incomplete studies.

Full-Fidelity Data as a Differentiator

OneMedNet positions its data offering around the concept of full-fidelity DICOM studies — complete imaging series with intact pixel data and acquisition metadata. The company de-identifies each study at the source while preserving the diagnostic fidelity required for Training foundation models. This approach is designed to address a challenge in the AI development space, where compressed or stripped-down secondary data may not meet the standards needed for clinically reliable model outputs.

The studies delivered under the agreement cover a diverse range of patient demographics, anatomical regions, scanner manufacturers, and acquisition protocols. These characteristics are described as essential for training generalizable and clinically reliable AI models. OneMedNet sources its data from a live network of more than 2,300 healthcare partner sites, applying de-identification at scale.

Market Context for AI-Ready Medical Data

The demand for high-quality medical imaging data has grown in parallel with the expansion of foundation model development in healthcare. Companies building AI systems for radiology require large volumes of diverse, high-fidelity imaging data to train models that can generalize across patient populations, imaging modalities, and clinical settings. OneMedNet's ability to deliver de-identified, source-level data at scale positions it within this emerging Supply chain.

The company's iRWD platform underpins its data operations, supporting applications ranging from Drug Development and regulatory submissions to foundational AI model training. With a network encompassing over 90 million patient journeys and 270 million studies, the platform's scale provides a data reservoir that few competitors can match.

The Takeaway

OneMedNet's re-order from a foundation model customer illustrates a Recurring Revenue dynamic in the AI training data market. As AI developers move from initial model development to scaled training and refinement, their demand for high-quality, diverse data tends to grow. OneMedNet's direct sourcing model, combined with its focus on preserving diagnostic fidelity during de-identification, appears to resonate with customers operating at the frontier of medical AI development. The key question going forward is whether the company can replicate this repeat-order pattern across a broader customer base while maintaining the quality standards that differentiate its offering.