arXiv:2607.08625v1 Announce Type: cross 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 w
The proliferation of consumer-facing health chatbots, coupled with the inherent complexities of real-world patient interactions, makes this analysis timely for refining AI in healthcare.
This research highlights crucial limitations in current AI development and evaluation for sensitive applications like healthcare, demanding more robust and realistic simulators, which impacts patient safety and trust.
The understanding of patient-chatbot interactions deepens, moving beyond simulated environments to real-world complexities, which will inform more authentic and effective AI in healthcare.
- · AI healthcare developers focusing on realism
- · Patients receiving more empathetic care
- · Companies offering advanced AI simulation tools
- · Chatbot developers relying solely on synthetic data
- · Healthcare providers with poorly tested AI tools
- · Generative AI models without nuanced interaction capabilities
Improved patient communication within AI health applications leading to better diagnostic support and patient experience.
Increased regulatory scrutiny and new standards for AI healthcare tools based on real-world interaction data.
A shift towards 'empathy-aware' AI design principles across various sectors requiring nuanced human interaction.
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Read at arXiv cs.CL