AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
- URL: http://arxiv.org/abs/2606.27302v1
- Date: Thu, 25 Jun 2026 17:24:26 GMT
- Title: AI Healthcare Chatbots as Information Infrastructure: A Large-Scale Study of User-Reported Breakdowns
- Abstract summary: This study examines over 15,000 user reviews from 59 AI healthcare chatbots apps.<n>We identify three recurring breakdowns: access barriers and service unreliability, user experience and interaction quality, and billing and customer support issues.<n>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.
- Score: 3.4874087337090383
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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 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.
Related papers
- On the Security and Privacy of AI-based Mobile Health Chatbots [0.24554686192257424]
This study empirically assesses 16 AI-based mHealth chatbots identified from the Google Play Store.<n>Our findings revealed security vulnerabilities, privacy issues, and non-compliance with Google Play policies.<n>These recommendations focus on improving data handling processes, disclosure, and user security.
arXiv Detail & Related papers (2025-11-15T22:49:07Z) - Therapeutic AI and the Hidden Risks of Over-Disclosure: An Embedded AI-Literacy Framework for Mental Health Privacy [3.602377086789099]
Large Language Models (LLMs) are increasingly deployed in mental health contexts.<n>LLMs lack a clear structure for what information is collected, how it is processed, and how it is stored or reused.<n>We propose a framework embedding Artificial Intelligence (AI) literacy interventions directly into mental health conversational systems.
arXiv Detail & Related papers (2025-10-12T20:50:06Z) - Decoding User Concerns in AI Health Chatbots: An Exploration of Security and Privacy in App Reviews [1.2437039433843042]
This study evaluates the effectiveness of automated methods, specifically BART and Gemini GenAI, in identifying security privacy related (SPR) concerns.<n>Our results indicate that while Gemini's performance in SPR classification is comparable to manual labeling, both automated methods have limitations.
arXiv Detail & Related papers (2025-01-31T00:38:37Z) - MisinfoEval: Generative AI in the Era of "Alternative Facts" [50.069577397751175]
We introduce a framework for generating and evaluating large language model (LLM) based misinformation interventions.
We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users.
Our findings confirm that LLM-based interventions are highly effective at correcting user behavior.
arXiv Detail & Related papers (2024-10-13T18:16:50Z) - On the Reliability of Large Language Models to Misinformed and Demographically-Informed Prompts [20.84000437261526]
We investigate and observe Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information.
quantitative analysis using True/False questions reveals that these chatbots can be relied on to give the right answers to these close-ended questions.
qualitative insights, gathered from domain experts, shows that there are still concerns regarding privacy, ethical implications.
arXiv Detail & Related papers (2024-10-06T07:40:11Z) - Designing Chatbots to Support Victims and Survivors of Domestic Abuse [2.3020018305241337]
We investigate the role that chatbots may play in supporting victims/survivors in situations such as these or where direct access to help is limited.
Interviews were conducted with experts working in domestic abuse support services and organizations.
Thematic content analysis was applied to assess and extract insights from the interview data and the content on victim-support websites.
arXiv Detail & Related papers (2024-02-27T10:40:15Z) - Conversational Health Agents: A Personalized LLM-Powered Agent Framework [1.4597673707346281]
Conversational Health Agents (CHAs) are interactive systems that provide healthcare services, such as assistance and diagnosis.
We propose openCHA, an open-source framework to empower conversational agents to generate a personalized response for users' healthcare queries.
openCHA includes an orchestrator to plan and execute actions for gathering information from external sources.
arXiv Detail & Related papers (2023-10-03T18:54:10Z) - A Comprehensive Picture of Factors Affecting User Willingness to Use
Mobile Health Applications [62.60524178293434]
The aim of this paper is to investigate the factors that influence user acceptance of mHealth apps.
Users' digital literacy has the strongest impact on their willingness to use them, followed by their online habit of sharing personal information.
Users' demographic background, such as their country of residence, age, ethnicity, and education, has a significant moderating effect.
arXiv Detail & Related papers (2023-05-10T08:11:21Z) - Multi-task Learning for Personal Health Mention Detection on Social
Media [70.23889100356091]
This research employs a multitask learning framework to leverage available annotated data to improve the performance on the main task.
We focus on incorporating emotional information into our target task by using emotion detection as an auxiliary task.
arXiv Detail & Related papers (2022-12-09T23:49:00Z) - What Do End-Users Really Want? Investigation of Human-Centered XAI for
Mobile Health Apps [69.53730499849023]
We present a user-centered persona concept to evaluate explainable AI (XAI)
Results show that users' demographics and personality, as well as the type of explanation, impact explanation preferences.
Our insights bring an interactive, human-centered XAI closer to practical application.
arXiv Detail & Related papers (2022-10-07T12:51:27Z) - Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users [51.644376281196394]
When using medical images for diagnosis, it is important that the images are of high quality.<n>In telemedicine, a common problem is that the quality issue is only flagged once the patient has left the clinic, meaning they must return in order to have the exam redone.<n>This paper reports on the development of an AI system for flagging and explaining low-quality medical images in real-time.
arXiv Detail & Related papers (2022-07-06T14:53:26Z) - Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state.
The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
arXiv Detail & Related papers (2020-09-21T03:45:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.