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others, we use imaging biomarkers as endpoints to measure treatment response or disease progression.
Our tools standardize measurements like lung nodule size, volume, and growth across different sites and patient groups. The consistency makes data easier to compare, which is crucial for regulatory submissions and statistical analysis.
In infectious disease research, we’ re supporting a tuberculosis trial where our AI quantifies pulmonary cavities. The system measures cavity count and size, helping with patient stratification and tracking treatment response. This type of standardized imaging data enables trials run more efficiently.
How does Qure. ai adapt AI for different healthcare systems globally? Deploying AI in over 105 countries means staying flexible. We train our algorithms on more than a billion data sets from diverse regions, imaging types, age groups, and populations. This broad approach ensures our AI performs well in any care setting, from bigcity hospitals to rural clinics.
Having the right representation in our training data is key when AI enters frontline care. We build our systems to fit within any existing infrastructure, whether it’ s advanced or more limited.
What innovations is Qure. ai working on next? In early 2026, we received a multi-milliondollar grant from the Gates Foundation to advance diagnostics for tuberculosis and pneumonia in under-resourced areas. We’ re partnering with the World Health Organisation to support global lung health diagnostic pathways.
The database will feature de-identified clinical histories, chest X-rays, thoracic ultrasounds, high-resolution CT scans, cough
We’ re partnering with the World Health Organisation to support global lung health diagnostic pathways
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