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

TL;DR

Addresses how the public uses LLMs for health information and the ethical implications of AI-assisted healthcare. A mixed-methods design with surveys (N=214) and interviews (N=17) maps access patterns, motivations, and concerns. Findings show LLMs occupy a niche between search engines and online communities, offering rapid, structured information while prompting cross-validation and raising concerns about misinformation and doctor-patient dynamics; many participants endorse LLM-assisted workflows for routine information and auxiliary clinical tasks, with caveats for serious conditions. The work informs public guidance, clinician practices, and future development of trusted, up-to-date medical LLMs for public health.

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.