# Understanding the concerns and choices of public when using large language models for healthcare

arXiv: https://arxiv.org/abs/2401.09090 · HTML: https://mlsanity.com/paper/2401.09090/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2024.

## Built on by (most cited first, 3 of 3)

- [This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA](https://mlsanity.com/paper/2604.05051/) (2026)
- [Plausibility as Failure: How LLMs and Humans Co-Construct Epistemic Error](https://mlsanity.com/paper/2512.16750/) (2025)
- [LLMs for health: Perceived benefits, risks, intention to use AI chatbots, and willingness to self-disclose across sensitive health topics](https://mlsanity.com/paper/2607.09253/) (2026)

## Abstract (arXiv)

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.