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The complexities of patient-centred conversational artificial intelligence

arXiv 2607.08625 ↗First posted 9 Jul 2026cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

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References · 14

  1. 2023Towards Understanding Sycophancy in Language Modelscited by 1,018
  2. 2017Counterfactual Fairnesscited by 804
  3. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  4. 2024Large language models that replace human participants can harmfully misportray and flatten identity groupscited by 141
  5. 2025SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviorscited by 52
  6. 2025Large Language Models Pass the Turing Testcited by 48
  7. 2025PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactionscited by 30
  8. 2025Advancing Conversational Diagnostic AI with Multimodal Reasoningcited by 20
  9. 2024LLM Targeted Underperformance Disproportionately Impacts Vulnerable Userscited by 15
  10. 2025AI Agents for Conversational Patient Triage: Preliminary Simulation-Based Evaluation with Real-World EHR Datacited by 5
  11. 2026A Scoping Review of LLM-as-a-Judge in Healthcare and the MedJUDGE Frameworkcited by 3
  12. 2024Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinkingcited by 2
2 more
  1. 2026Code Sharing In Prediction Model Research: A Scoping Reviewcited by 1
  2. 2026Evaluating the Utility of Personal Health Records in Personalized Health AIcited by 1

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