Intention to use AI health chatbots and willingness to self-disclose track perceived benefits/risks and individual traits more than physical-vs-psychological topic type, with only a small sensitivity effect on intention.
Understanding the concerns and choices of public when using large language models for healthcare
1 Pith paper cite this work, alongside 77 external citations. Polarity classification is still indexing.
abstract
Large language models (LLMs) have shown their potential in biomedical fields. However, how the public uses them for healthcare purposes such as medical Q\&A, self-diagnosis, and daily healthcare information seeking is under-investigated. This paper adopts a mixed-methods approach, including surveys (N=214) and interviews (N=17) to investigate how and why the public uses LLMs for healthcare. We found that participants generally believed LLMs as a healthcare tool have gained popularity, and are often used in combination with other information channels such as search engines and online health communities to optimize information quality. Based on the findings, we reflect on the ethical and effective use of LLMs for healthcare and propose future research directions.
fields
cs.HC 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
LLMs for health: Perceived benefits, risks, intention to use AI chatbots, and willingness to self-disclose across sensitive health topics
Intention to use AI health chatbots and willingness to self-disclose track perceived benefits/risks and individual traits more than physical-vs-psychological topic type, with only a small sensitivity effect on intention.