AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
arXiv:2606.27302v1 Announce Type: cross Abstract: 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 an
The proliferation of AI-powered healthcare chatbots is rapidly increasing access to health information, making their performance and user experience a pressing concern as adoption scales.
As AI healthcare chatbots become a primary interface for health information, their reliability and user-reported breakdowns directly impact public health outcomes and trust in AI systems.
The focus for AI healthcare applications shifts from mere deployment to understanding and mitigating user-reported issues across informational, emotional, and infrastructural dimensions.
- · AI ethicists
- · Healthcare UX designers
- · Regulatory bodies
- · Companies with robust AI governance
- · Developers of unreliable AI healthcare chatbots
- · Healthcare providers with poor customer support
- · Patients relying on flawed AI systems
This study provides a critical framework for evaluating and improving AI healthcare chatbot performance based on large-scale user feedback.
Increased scrutiny and demand for higher quality AI healthcare applications could lead to new industry standards and regulatory frameworks.
Public perception of AI in healthcare, either positive or negative, will be significantly shaped by the industry's response to these documented breakdowns.
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Read at arXiv cs.AI