Use Case
Clinical Chatbot Evaluation
Overview
System Description
Clinical chatbots provide medical information, symptom assessment, and health guidance directly to patients or healthcare professionals. 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.
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Risk Profile by Setting
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.
Methodology
Evaluation Workflow
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.
Safety
Top Failure Modes
The most common and dangerous failure modes for this type of medical AI system.
Related
Other Use Cases
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