Active clinical AI deployments
We deploy ethically validated machine learning models directly into under-resourced public health systems, bridging the gap between academic research and frontline clinical utility.
Deployments in action
Our active projects focus on addressing diagnostic gaps and ensuring algorithmic equity across diverse clinical environments.
Ophthalmic Screening
Respiratory Diagnostics
Maternal Risk Triage
Deploying automated retinal assessment models in community clinics to identify diabetic retinopathy early, preventing preventable vision loss.
Integrating explainable chest X-ray classifiers within public health centers to accelerate triage for tuberculosis and acute respiratory infections.
Validating low-resource predictive models that assist midwives in identifying high-risk pregnancies during routine antenatal community visits.
Co-design in practice
Frontline clinical utility
We do not develop algorithms in isolation. Our engineers work directly alongside clinical practitioners to ensure every tool fits the physical constraints and workflow realities of under-resourced clinics.
This collaborative validation process guarantees high adoption rates, clear model explainability, and immediate clinical relevance where diagnostic expertise is most scarce.
Advance equitable health
We partner with academic institutions, NHS trusts, and global health grantmakers to validate and scale ethical AI tools. Join our open-source clinical research network.