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Solution · Pillar 01

Clinical intelligence for the healthcare AI lifecycle

From ground-truth development to GenAI evaluation and independent validation, Curota Health integrates structured clinical expertise into healthcare AI development — turning model outputs into defensible clinical evidence.

Medical GenAI & LLM Evaluation

Healthcare LLMs require more than linguistic evaluation. They require clinical judgment. Curota Health supports structured evaluation across the dimensions that actually determine patient safety.

Clinical correctness
Hallucination detection
Completeness
Clinical relevance
Patient-safety risk
Medical reasoning quality
Guideline alignment
Expert escalation on ambiguity
Applications
Ambient clinical documentationMedical copilotsClinical summarizationPatient communicationDiagnostic supportHealthcare chatbotsMedical search & retrieval

Human Feedback & Medical RLHF

Build clinically meaningful human-feedback pipelines using qualified healthcare reviewers — reward-model data grounded in real medical judgment, not crowd labeling.

Preference ranking
Response comparison
Clinical scoring rubrics
Safety evaluation
Reward-model data generation
Expert escalation
Specialty adjudication
Reviewer agreement analysis

Clinical Ground-Truth Data

High-quality medical AI begins with defensible ground truth — multi-reader consensus with full provenance, not a single opinion or a crowd vote.

Radiology
X-ray · CT · MRI · Ultrasound
Pathology
WSI · Histology · Cytology
Cardiology
ECG · Echocardiography
Ophthalmology
Fundus · OCT · Retinal
Clinical NLP
Records · Notes · Transcripts
Services
AnnotationClassificationSegmentationClinical abstractionExpert reviewConsensus workflowsAdjudication

Independent Model Validation

Support developers in generating structured clinical evidence for AI evaluation and regulatory preparation. Curota Health supports regulatory evidence development — it is not a regulatory authority or a guarantee of approval.

Locked validation datasets
Representative test-set development
Sensitivity / specificity analysis support
Subgroup analysis
Bias evaluation
Error taxonomy
Clinical failure-mode analysis
Traceable evidence packages

Post-Deployment Model Monitoring

AI performance can change after deployment. Curota Health supports recurring clinical evaluation designed to identify problems before they reach patients.

Performance drift
Population variation
Hardware-related variation
Clinical workflow changes
New failure patterns
Subgroup performance differences

Your technology has intelligence.
Give it clinical intelligence.

Talk to Curota Health