AI Safety
Clinical AI Safety Framework
Medical AI systems can fail in ways that cause real patient harm. Our structured taxonomy classifies 10 failure modes with severity ratings, clinical impact analysis, and detection methodology — built by clinicians, for clinicians.
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Failure categories
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Critical severity
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High severity
Clinician-Led
Methodology
The clinical firewall
Every AI-generated medical content passes through our clinical firewall before any human evaluator sees it. The firewall validates drug dosages against BNF ranges, checks for real patient data patterns, flags scope violations, and ensures all scenarios include appropriate safety disclaimers.
This is not just content moderation — it is a structured clinical validation pipeline that catches the specific failure modes medical AI systems exhibit in practice.
Failure mode categories
Each category represents a distinct class of medical AI failure with specific severity classification and clinical impact.
Hallucinated Diagnosis
highAI invents conditions not supported by evidence or patient presentation
Clinical impact: Unnecessary anxiety, investigations, or treatment
Dangerous Dosing
criticalIncorrect drug doses that could cause harm
Clinical impact: Toxicity, organ damage, death
Scope Violation
highProviding definitive diagnosis without sufficient clinical data
Clinical impact: Misdiagnosis, delayed appropriate care
Emergency Underestimation
criticalFailing to recognise or appropriately escalate red flag symptoms
Clinical impact: Delayed emergency treatment, death
Contraindication Ignored
criticalPrescribing or recommending drugs unsafe for the patient
Clinical impact: Adverse drug reactions, teratogenicity, death
Multi-Factor Contraindication
criticalComplex drug interaction chains that require considering multiple factors
Clinical impact: Organ failure, bleeding, serotonin syndrome, cardiac arrest
Guideline Contradiction
highAdvice that conflicts with current NICE or BNF guidelines
Clinical impact: Suboptimal treatment, delayed effective care
Outdated Information
moderateUsing superseded clinical guidance or withdrawn medications
Clinical impact: Inappropriate treatment, missed safety signals
Dosage Frequency/Route Error
criticalCorrect drug but wrong frequency, route, or administration method
Clinical impact: Toxicity from overdosing frequency, treatment failure
False Reassurance
criticalInappropriately reassuring when escalation or urgent referral is needed
Clinical impact: Delayed cancer diagnosis, missed sepsis, death
For AI companies
If you are building or deploying medical AI systems, our safety framework provides the structured evaluation methodology your team needs.
Structured Red-Teaming
Adversarial testing across all 10 failure categories by domain-expert clinicians.
Statistical Quality
Clinical Evaluation
Statistically calibrated evaluators with confidence intervals on every metric.
Findings by Severity
Critical
High
Moderate
Low
Safety Reports
Detailed reports with failure mode coverage, severity distribution, and mitigation recommendations.
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