AI Developers
Develop and train AI models faster and more
effectively with OneMedNet’s Imaging
Real-World Data (iRWD™).
Expedite training and validation of your model
AI models, including those developed for medical imaging, are advancing diagnostics and patient care. Deep learning algorithms can extract insight from medical images at an untold level of detail.
Training these models requires vast amounts of clinical data. OneMedNet equips your AI model
with rich, heterogenous, and regulatory-grade iRWD™ precisely curated to your specifications — often expediting the clinical trials needed to validate your model.
RWD for AI model development
OneMedNet’s iRWD™ can accelerate the development of your imaging model by curating datasets for AI training and validation.
With our data, you can simultaneously pursue multiple use cases for your model and leverage our curation skills and longitudinal record access to build deeper knowledge-based efficiencies (e.g., implementing probabilistic labels).
CASE STUDY
How our RWD fueled one company’s cancer research
Our customer wanted to train their algorithm to detect lung cancer sooner — spotting early signs through X-ray images instead of requiring CT scans.
- THEIR SPECIFICATIONS
They requested considerable patient report information, including tumor size specifications, lesion location, as well as race and ethnicity population ranges from multiple data source sites.
They also asked for metadata for all biopsy, cytology, or histology of specimen within 12 months of initial screening.
- OUR PROCESS
We converted unstructured data into structured data, reconciling longitudinal and disparate patient record information and harmonizing site-dependent and year-to-year data formatting inconsistencies.
Our curation experts confirmed the results was highly tailored RWD with exact matches for cohort specifications.
- THE RESULT
We provided 3,000 perfectly curated images (1500 screening positives, 1500 screening negatives) to train their model, including X-ray images and CT images with and without cancerous lung modules.