AI that supports clinicians.
Not replaces them.
We build clinical decision AI that is explainable by design, validated against real-world outcomes, and integrated into the workflows clinicians already use. Built for hospital systems and CROs.
Discuss your clinical AI projectClinical AI applications
Diagnostic Support
AI that surfaces differential diagnoses, flags missed findings, and highlights anomalies in lab results - supporting, not replacing, clinical judgment.
Treatment Recommendations
Evidence-based treatment pathway suggestions aligned to clinical guidelines, patient history, and co-morbidity profiles.
Risk Stratification
Population health models that identify high-risk patients for proactive outreach, early intervention, and care coordination.
Drug Interaction Checking
Real-time polypharmacy checking against curated drug databases, patient-specific allergy profiles, and renal/hepatic function adjustments.
Imaging Analysis
Deep learning models for radiology, pathology, and retinal imaging - trained on clinically validated datasets with radiologist-in-the-loop review.
Care Pathway Optimisation
Process mining and AI-guided workflow analysis to reduce unnecessary variation in care delivery and improve patient flow.
The explainability requirement
Black-box AI does not work in clinical settings. A clinician who cannot understand why a model flagged a finding cannot act on it safely - and regulators will not clear a system that cannot explain its reasoning.
Every clinical AI system we build includes SHAP-based feature attribution, attention visualisation for imaging models, confidence intervals on predictions, and plain-language reasoning summaries presented at the point of care.
Clinical validation process
Integration with clinical workflows
EHR Hooks
CDS Hooks standard for real-time decision support triggered by EHR events - order entry, problem list updates, and encounter start.
Alert System Design
We design alerts that minimise fatigue: tiered severity, actionable recommendations, one-click acknowledgement, and default dismissal for low-confidence signals.
Clinician UX
Clinical AI UX is different from consumer UX. We involve clinicians in usability testing, design for time-pressured environments, and measure adoption as a first-class metric.
Bias & fairness
Algorithmic bias in clinical AI can cause real patient harm. Here is how we test for it and what we do when we find it.
Demographic stratification
Performance evaluated separately across age, sex, race, and ethnicity sub-groups to detect differential accuracy.
Dataset audit
Training data reviewed for historical encoding biases - labels assigned by biased clinical practice can propagate into model outputs.
Calibration testing
Predicted probabilities calibrated against actual outcome rates across population segments, not just aggregate AUC.
Counterfactual testing
Model outputs tested for sensitivity to protected attribute changes - a valid model should not change predictions based on race alone.
"Risk stratification model identifying high-risk readmission patients - 28% reduction in 30-day readmissions."
Deployed across a 600-bed hospital system. Retrospective validation on 3 years of patient records. SHAP explainability surfaced to care coordinators via Epic CDS Hooks integration.
How clinical decision AI moves from raw EHR data to a clinician recommendation.
EHR Data Sources
Labs & vitals
Medications
Problem list
ADT events
FHIR Ingestion
R4 resource normalisation
HL7 v2 parsing
PHI de-identification
Schema validation
Clinical AI Models
Risk stratification
Differential diagnosis
Drug interaction check
SHAP attribution
CDS Output
CDS Hooks card
Alert severity tier
Confidence interval
Override logging
Clinician Review
EHR-embedded alert
One-click accept/dismiss
Feedback captured
Audit trail written
Clinical AI impact where the evidence has been collected.
28%
reduction in 30-day readmissions with risk stratification model
91%
clinician alert acceptance rate when SHAP attribution is shown
3 yrs
retrospective validation horizon used before any go-live deployment
< 2s
CDS Hooks response latency on real-time risk scoring at point of care
“The model surfaces high-risk readmission patients at discharge - care coordinators now act within 2 hours instead of 2 days. We saw a 28% reduction in 30-day readmissions in the first six months.”
28%
Reduction in 30-day readmissions
3 years
Patient records in retrospective validation
< 30 min
Time to flagging from discharge
Live
Epic CDS Hooks integration
Clinical AI questions we answer before the project starts.
How do you handle alert fatigue in clinical AI deployments?
Alert fatigue is the primary adoption killer in clinical AI. We design tiered alert systems - high-confidence findings surface prominently with actionable recommendations; low-confidence signals go to dashboards rather than interruptions. We measure override rates from day one and recalibrate thresholds continuously. One-click accept with auto-documentation reduces friction for high-value alerts.
What datasets do you use to train clinical AI models?
We work with your institution's de-identified patient records as the primary training data. Where available, we supplement with public clinical datasets (MIMIC-IV, PhysioNet, UK Biobank) for pre-training or benchmarking. We never use synthetic data as a substitute for clinical validation on real patient populations. All training data governance is documented as part of the compliance evidence pack.
Can clinical AI replace clinical judgment?
No - and we do not design it to. Clinical AI is decision support, not decision replacement. Our systems are built to surface evidence, flag anomalies, and present options - with a clinician always in the loop. Architecturally, every output includes a confidence interval and the model's top contributing features. Clinicians can override any recommendation, and all overrides are logged for model improvement.
How do you address demographic bias in clinical AI models?
We evaluate model performance separately across age, sex, race, and ethnicity sub-groups during validation. We audit training labels for historically biased clinical practice. We run calibration testing per demographic segment and counterfactual sensitivity tests for protected attributes. Where performance gaps exceed clinical thresholds, we address them through targeted data augmentation or model retraining before deployment.