Clinical Decision Support
Models that augment clinician judgment at the point of care — surfacing risk signals, guideline-aligned recommendations, and transparent reasoning paths.
Decision SupportIntelligence at the point of care — and at the heart of the pipeline.
From clinical decision support to predictive epidemiology, our AI work is engineered to make healthcare systems faster, safer, and more equitable — while respecting the privacy and dignity of every patient.
Our AI thesis
Computation that respects clinical reality.
We build artificial intelligence to support — not replace — clinical judgment. Every model we deploy is auditable, evaluated for population fairness, and grounded in real-world clinical outcomes. We treat patient privacy as a non-negotiable design constraint, not a downstream consideration.
- Transparent and auditable model architectures
- Bias and fairness evaluations across patient populations
- Privacy-preserving training and inference pipelines
- Continuous validation against real-world outcomes
Our Standard
0compromise
on patient privacy, clinical safety, or scientific integrity — across every model, dataset, and deployment.
Capabilities
Five areas where AI is changing what's possible.
Our applied AI program focuses on the points in the healthcare system where computational intelligence delivers measurable benefit to clinicians, researchers, and patients.
Predictive Analytics
Population-level forecasting for chronic-disease prevention, resource allocation, and proactive public-health response.
PredictiveReal-World Evidence
Pipelines that translate longitudinal clinical data into therapeutic insight — closing the loop between approved drugs and clinical reality.
EvidenceUnified Health Infrastructure
National-scale digital health architecture engineered for equity, continuity of care, and rapid response to public-health emergencies.
InfrastructurePharmacogenomic Decision Tools
Genomics-aware prescribing support to reduce adverse drug events and personalize therapeutic selection at scale.
GenomicsHealthcare Informatics
Integration layers that connect clinical, genomic, and operational data — enabling research and care across previously siloed systems.
Informatics
AI principles
Four commitments we hold ourselves to.
Principle 01
Transparent
Every model in clinical use has documented training data, performance characteristics, and known limitations.
Principle 02
Equitable
We evaluate and mitigate performance disparities across demographic and clinical subpopulations before deployment.
Principle 03
Private
Patient data is protected end-to-end with privacy-preserving techniques and minimal-collection principles.
Principle 04
Accountable
Human clinical oversight is required for every deployed decision-support system. AI augments — never replaces — clinicians.