The complexities of patient-centred conversational artificial intelligence
Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment. However, chatbot development and evaluation often rely on cooperative, articulate, simulated patients. We analysed 2,053 real patient-chatbot conversations and found that communication patterns and expression of emotions vary widely across users. We developed a patient simulator that separately models clinical content, emotional state, conversational strategy, and communication style. In a Turing-inspired evaluation of realism with 15 human graders, simulated conversations were nearly indistinguishable from real ones, with human graders achieving an accuracy of 55%. We use...
| Authors | João Matos, Olivia Buege, Donny Cheung et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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