Osseus.ai is building medical superintelligence by creating an EHR platform and clinical datasets designed to make AI work reliably across diverse patient populations. The company partners with hospitals and clinics worldwide to collect consented, representative data, then uses that foundation to measure and improve model performance across demographic groups. Their approach addresses the systemic underrepresentation in clinical AI training data, which currently causes models to perform worse for minority patients and lose significant predictive power when applied across populations.
Funding
Funding not disclosed
Founders
Product
Problem
Most clinical AI is trained on data from a handful of high-income countries, leaving many patient populations underrepresented. This narrow training base causes models to perform worse for minority groups, with chest X-ray classifiers underdiagnosing Black, Hispanic, and female patients, and risk scores losing up to 61% of predictive power when applied across different ethnic populations. These performance gaps represent a fundamental failure of trustworthiness in medical AI, not merely a secondary fairness issue.
Solution
Osseus.ai builds medical superintelligence by rebuilding the EHR specifically for AI development and deployment. The company partners with hospitals and clinics worldwide to collect consented imaging, notes, labs, and outcomes into structured patient timelines, creating representative clinical datasets that better reflect the populations AI will serve. These datasets power realistic training environments where models are measured by demographic group and care setting, allowing Osseus to identify and reduce performance gaps before clinical deployment. Clinicians run on the Osseus EHR or connect their existing record systems, with all models deployed under clinical supervision within an agreed governance framework. The company tracks industry-wide progress on public benchmarks, noting that leading systems currently complete only 36-55% of whole-workflow tasks, and uses this foundation to bring medical AI into care with clinicians in control.
Target Audience
Primary customers are hospitals, clinics, and healthcare systems worldwide that want to contribute to or deploy medical AI trained on representative patient populations, as well as clinical AI developers seeking reliable, well-governed training data and evaluation environments.
Features
- Purpose-built EHR platform that structures consented imaging, notes, labs, and outcomes into unified patient timelines for AI training and evaluation
- Global hospital and clinic partnerships that build representative clinical datasets, addressing the fact that over half of published clinical AI datasets come from just two countries
- Demographic-group performance measurement that quantifies model accuracy across patient populations and care settings to identify reliability gaps
- Realistic training environments that test models on complete clinical workflows rather than isolated tasks, addressing the gap where leading systems finish only 36-55% of benchmark tasks
- Public medical and biological leaderboard tracking frontier model performance across published benchmarks
- Governance framework governing data consent, de-identification, licensing, and revocation for all clinical data partnerships