ArticleJMIR AI2025
Assessing the Capability of Large Language Models for Navigation of the Australian Health Care System: Comparative Study.
Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Australians can face significant challenges in navigating the health care system, especially in rural and regional areas. Generative search tools, powered by large language models (LLMs), show promise in improving health information retrieval by generating direct answers. However, concerns remain regarding their accuracy and reliability when compared to traditional search engines in a health care context. Objective: This study aimed to compare the effectiveness of a generative artificial intelligence (AI) search (ie, Microsoft Copilot) versus a conventional search engine (Google Web Search) for navigating health care information. Methods: A total of 97 adults in Queensland, Australia, participated in a web-based survey, answering scenario-based health care navigation questions using either Microsoft Copilot or Google Web Search. Accuracy was assessed using binary correct or incorrect ratings, graded correctness (incorrect, partially correct, or correct), and numerical scores (0-2 for service identification and 0-6 for criteria). Participants also completed a Technology Rating Questionnaire (TRQ) to evaluate their experience with their assigned tool. Results: Participants assigned to Microsoft Copilot outperformed the Google Web Search group on 2 health care navigation tasks (identifying aged care application services and listing mobility allowance eligibility criteria), with no clear evidence of a difference in the remaining 6 tasks. On the TRQ, participants rated Google Web Search higher in willingness to adopt and perceived impact on quality of life, and lower in effort needed to learn. Both tools received similar ratings in perceived value, confidence, help required to use, and concerns about privacy. Conclusions: Generative AI tools can achieve comparable accuracy to traditional search engines for health care navigation tasks, though this did not translate into an improved user experience. Further evaluation is needed as AI technology improves and users become more familiar with its use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.