Use Case
Clinical Chatbot Evaluation
Conversational clinical AI walks the line between helpful and harmful.
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Clinical chatbots provide medical information, symptom assessment, and health guidance directly to patients or healthcare professionals.
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These conversational AI systems must maintain clinical accuracy across multi-turn dialogues, handle ambiguous queries safely, provide appropriate disclaimers, and avoid generating responses that could be mistaken for personalised medical advice when they lack sufficient clinical context.
What we test against
Whether the user is a clinician, a patient, or a carer changes both the failure surface and the consequence of getting it wrong.
Patient-facing chatbots carry the highest risk because users may act on AI advice without consulting a healthcare professional. The risk is compounded by conversational dynamics — users may provide incomplete information, ask follow-up questions that push the AI beyond its competence, or interpret hedged language as confident recommendations. Professional-facing clinical chatbots have lower but still significant risk, particularly around hallucinated references, fabricated guidelines, and confidently incorrect dosing information.
How we evaluate this system.
A structured assessment matrix tailored to this AI system type — calibrated evaluators, severity-weighted scoring, and statistical confidence intervals on every metric.
Our chatbot evaluation framework tests conversational AI through structured multi-turn scenarios designed by clinicians. Evaluators assess clinical accuracy, safety of advice, appropriate use of disclaimers, escalation to human professionals, and handling of edge cases. We specifically test for conversation drift — where the AI starts safe but gradually provides increasingly specific advice beyond its competence as the conversation progresses.
Coverage matrix
Structured
Evaluators
Calibrated
Confidence intervals
Every metric
Top failure modes we test
Each engagement systematically probes these failure modes with severity-weighted scoring and clinical impact analysis.
False Reassurance
A core failure mode for this AI system class. Tested through adversarial prompts, severity-weighted gold tasks, and clinician-led red-team review — with coverage metrics reported back to your team.
Scope Creep
A core failure mode for this AI system class. Tested through adversarial prompts, severity-weighted gold tasks, and clinician-led red-team review — with coverage metrics reported back to your team.
Hallucinated Reference
A core failure mode for this AI system class. Tested through adversarial prompts, severity-weighted gold tasks, and clinician-led red-team review — with coverage metrics reported back to your team.
Inappropriate Personalisation
A core failure mode for this AI system class. Tested through adversarial prompts, severity-weighted gold tasks, and clinician-led red-team review — with coverage metrics reported back to your team.
Other medical AI systems
Each system type has distinct failure modes — but the evaluation methodology shares a common backbone.
Interested?
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