The complexities of patient-centred conversational artificial intelligence

2026-07-09

AI Summary

Current health chatbots often rely on idealized patient simulations, struggling to accommodate the diversity in real users’ expression, emotional states, and communication strategies, which can lead to triage bias. Addressing this limitation, this study leverages 2,053 real clinical dialogues to propose the first multidimensional patient simulator that explicitly models clinical content, emotional state, dialogue strategy, and communication style. These dimensions are systematically integrated into a large language model (LLM)-based triage evaluation framework. Experimental results show that dialogues generated by the simulator are nearly indistinguishable from real conversations in human evaluations (identification accuracy of only 55%). Moreover, varying communication styles significantly affect the urgency assessment accuracy of four leading LLMs, highlighting the critical role of communicative diversity in ensuring fairness in AI-driven triage.

Abstract

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 used five distinct patient personae, across 1,164 clinician-graded cases, to evaluate the performance of four LLMs in urgency assessment. We found that communication style can significantly alter triage outcomes. Patient-centred conversational artificial intelligence must accommodate communication diversity: systems designed for idealised, rather than realistic, interactions risk underperforming and amplifying health disparities when deployed in the real world.

Problem

• patient-centred AI
• health chatbots
• communication diversity
• symptom assessment
• health disparities

Innovation

• patient simulator
• communication diversity
• large language models
• symptom assessment
• conversational AI