Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jul 14, 2024
Date Accepted: Dec 25, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
A Comparison of Laypeople’s Use of Large Language Models vs. Search Engines for Health Queries: Survey Study
ABSTRACT
Background:
Search engines have transformed the way laypeople access health information. Large language models (LLMs) are poised to enable another shift in health information seeking.
Objective:
Characterize how laypeople’s early use of large language models (LLMs) for health information compares to the use of search engines.
Methods:
We conducted a screening survey about the use of LLMs and search engines, and a follow-up survey probed the use of LLMs compared to search engines for health queries with 281 U.S. participants recruited on Prolific.
Results:
LLMs were perceived as less useful than search engines for answering health-related questions but elicited less negative feelings in response to results, seemed more human, and were perceived as less biased.
Conclusions:
Search engines remain the main source of information for laypeople seeking health information, yet positive perceptions of LLMs suggest their use may grow. With greater public reliance on LLMs, it is increasingly important for clinicians and health organizations to partner with LLM providers to improve the quality of health-related output.
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