分析1.5万条用户评论，揭示医疗AI聊天机器人三大故障类型
AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
- 基于59款应用的1.5万条评论，识别出三类常见故障
- 访问障碍、交互体验差与账单支持问题最影响用户体验
- 隐私安全问题常引发负面评价，适合设计者和政策制定者参考
AI医疗聊天机器人被广泛用于健康信息获取与自我管理，但其性能与对用户的影响仍待深入研究。本研究分析了来自59款AI医疗聊天机器人应用的超过15,000条用户评论，探讨这些系统在日常信息与情感场景中的运作情况。通过主题建模与解释性分析，识别出三类反复出现的故障：访问障碍与服务不可靠性、用户体验与交互质量差、账单与客户支持问题。隐私与安全担忧与最负面的使用体验密切相关。将AI医疗聊天机器人视为信息基础设施，研究揭示了可及性、可用性与信任缺失对用户造成的实际影响，为设计者、政策制定者及信息专业人士提供了切实可行的改进方向。
原文摘要 · Abstract (English)
AI healthcare chatbots are increasingly used to support health information seeking and self-management, yet their performance and impact on users remains to be studied. This study examines over 15,000 user reviews from 59 AI healthcare chatbot apps to explore how these systems function in everyday informational and emotional contexts. Topic modeling and interpretive analysis identify three recurring breakdowns: access barriers and service unreliability, user experience and interaction quality, and billing and customer support issues. Privacy and security concerns are associated with the most negative experiences. By framing AI healthcare chatbots as information infrastructures, our findings highlight how failures in access, usability, and trust affect users, offering actionable insights for designers, policymakers, and information professionals aiming to improve digital health systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。