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<p>Artificial intelligence (AI) chatbots are increasingly used for self-triage and medical advice seeking. Accurate AI performance, however, hinges on how users interact with such consumer-facing applications. While previous research has identified reservations regarding AI-generated medical advice, earlier stages of human–AI interaction, such as how symptoms are communicated, remain largely unexplored. In a preregistered between-subject experiment (<i>n</i> = 500), participants were randomly assigned to provide simulated symptom reports for common medical conditions to either an AI chatbot or a human physician. We evaluated the quality of the reports for an initial medical urgency assessment using physician-validated large language model-based suitability metrics. Participants who believed they were interacting with an AI tool (versus a physician) provided lower-quality symptom reports for medical triage. Our findings indicate a bias in how users communicate symptoms in digital settings. This outcome could compromise the performance of consumer-facing AI tools in real-world applications, regardless of the underlying model’s actual capacity.</p>

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Reduced symptom reporting quality during human–chatbot versus human–physician interactions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月1日

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  • Moritz Reis,
  • Florian Reis,
  • Yeun Joon Kim,
  • Aylin Demir,
  • Jess Lim,
  • Matthias I. Gröschel,
  • Sebastian D. Boie,
  • Wilfried Kunde

摘要

Artificial intelligence (AI) chatbots are increasingly used for self-triage and medical advice seeking. Accurate AI performance, however, hinges on how users interact with such consumer-facing applications. While previous research has identified reservations regarding AI-generated medical advice, earlier stages of human–AI interaction, such as how symptoms are communicated, remain largely unexplored. In a preregistered between-subject experiment (n = 500), participants were randomly assigned to provide simulated symptom reports for common medical conditions to either an AI chatbot or a human physician. We evaluated the quality of the reports for an initial medical urgency assessment using physician-validated large language model-based suitability metrics. Participants who believed they were interacting with an AI tool (versus a physician) provided lower-quality symptom reports for medical triage. Our findings indicate a bias in how users communicate symptoms in digital settings. This outcome could compromise the performance of consumer-facing AI tools in real-world applications, regardless of the underlying model’s actual capacity.