AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
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Abstract (arXiv)
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.
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References · 4
- 2017Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a surveycited by 91
- 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 67
- 2023Privacy in Large Language Models: Attacks, Defenses and Future Directionscited by 51
- 2025Can I Trust This Chatbot? Assessing User Privacy in AI-Healthcare Chatbot Applicationscited by 2
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