Service
Clinical AI Red-Teaming
AI companies deploying medical AI in clinical settings who need to identify safety risks before they reach patients. Critical for pre-launch safety assessment, regulatory submissions, and ongoing safety monitoring of deployed systems.
Find the failures before patients do.
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We conduct structured adversarial testing of medical AI systems across 10 clinically-derived failure mode categories.
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Our red-team evaluators — trained clinicians — systematically probe for dangerous dosing recommendations, false reassurance, contraindication failures, hallucinated diagnoses, and other safety-critical failure modes.
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Each engagement produces a severity-weighted safety report with specific mitigation recommendations.
Every engagement, audit-ready.
Structured outputs you can take to clinical safety reviews, procurement, and regulators — with the underlying methodology referenced throughout.
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Structured adversarial testing across 10 failure mode categories
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Severity-weighted safety report with clinical impact analysis
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Specific mitigation recommendations per failure mode
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Coverage metrics showing which risk categories were tested
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Re-testing protocol for validating fixes
A clinician-developed taxonomy of medical AI failures — not generic adversarial prompts.
Our red-team methodology is built on a clinician-developed taxonomy of medical AI failures — not generic adversarial testing. Our evaluators understand how clinical AI fails in practice because they work in healthcare. They know which questions a GP would ask, which drug interactions a pharmacist would catch, and which triage decisions could harm patients.
Other services
Engagements often combine evaluation, annotation, red-teaming, and advisory across the medical AI lifecycle.