@Article{info:doi/10.2196/64290,
author="Mendel, Tamir
and Singh, Nina
and Mann, Devin M
and Wiesenfeld, Batia
and Nov, Oded",
title="Laypeople's Use of and Attitudes Toward Large Language Models and Search Engines for Health Queries: Survey Study",
journal="J Med Internet Res",
year="2025",
month="Feb",
day="13",
volume="27",
pages="e64290",
keywords="large language model; artificial intelligence; LLMs; search engine; Google; internet; online health information; United States; survey; mobile phone",
abstract="Background: Laypeople have easy access to health information through large language models (LLMs), such as ChatGPT, and search engines, such as Google. Search engines transformed health information access, and LLMs offer a new avenue for answering laypeople's questions. Objective: We aimed to compare the frequency of use and attitudes toward LLMs and search engines as well as their comparative relevance, usefulness, ease of use, and trustworthiness in responding to health queries. Methods: We conducted a screening survey to compare the demographics of LLM users and nonusers seeking health information, analyzing results with logistic regression. LLM users from the screening survey were invited to a follow-up survey to report the types of health information they sought. We compared the frequency of use of LLMs and search engines using ANOVA and Tukey post hoc tests. Lastly, paired-sample Wilcoxon tests compared LLMs and search engines on perceived usefulness, ease of use, trustworthiness, feelings, bias, and anthropomorphism. Results: In total, 2002 US participants recruited on Prolific participated in the screening survey about the use of LLMs and search engines. Of them, 52{\%} (n=1045) of the participants were female, with a mean age of 39 (SD 13) years. Participants were 9.7{\%} (n=194) Asian, 12.1{\%} (n=242) Black, 73.3{\%} (n=1467) White, 1.1{\%} (n=22) Hispanic, and 3.8{\%} (n=77) were of other races and ethnicities. Further, 1913 (95.6{\%}) used search engines to look up health queries versus 642 (32.6{\%}) for LLMs. Men had higher odds (odds ratio [OR] 1.63, 95{\%} CI 1.34-1.99; P<.001) of using LLMs for health questions than women. Black (OR 1.90, 95{\%} CI 1.42-2.54; P<.001) and Asian (OR 1.66, 95{\%} CI 1.19-2.30; P<.01) individuals had higher odds than White individuals. Those with excellent perceived health (OR 1.46, 95{\%} CI 1.1-1.93; P=.01) were more likely to use LLMs than those with good health. Higher technical proficiency increased the likelihood of LLM use (OR 1.26, 95{\%} CI 1.14-1.39; P<.001). In a follow-up survey of 281 LLM users for health, most participants used search engines first (n=174, 62{\%}) to answer health questions, but the second most common first source consulted was LLMs (n=39, 14{\%}). LLMs were perceived as less useful (P<.01) and less relevant (P=.07), but elicited fewer negative feelings (P<.001), appeared more human (LLM: n=160, vs search: n=32), and were seen as less biased (P<.001). Trust (P=.56) and ease of use (P=.27) showed no differences. Conclusions: Search engines are the primary source of health information; yet, positive perceptions of LLMs suggest growing use. Future work could explore whether LLM trust and usefulness are enhanced by supplementing answers with external references and limiting persuasive language to curb overreliance. Collaboration with health organizations can help improve the quality of LLMs' health output. ",
issn="1438-8871",
doi="10.2196/64290",
url="https://www.jmir.org/2025/1/e64290",
url="https://doi.org/10.2196/64290",
url="http://www.ncbi.nlm.nih.gov/pubmed/39946180"
}