ArticleRheumatology advances in practice2025
Adoption and perception of LLM-based chatbots in health care: an exploratory cross-sectional survey of individuals with rheumatic diseases.
Article in Rheumatology advances in practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The rapid mainstream uptake of artificial intelligence (AI) technologies, particularly large language model (LLM)-based chatbots, have sparked interest in their potential role in healthcare. Despite technological advancements, little is known about the current utilization of LLM chatbots among individuals with rheumatic diseases. This study aimed to investigate the adoption of and perceptions towards LLM chatbots among individuals with rheumatic disease, along with associated sociodemographic factors. Methods: An exploratory cross-sectional survey was conducted with participants recruited both online, via Arthritis Care Experts' digital and social media platforms, and in person from rheumatology clinics in Edmonton, AB, Canada. Respondents completed an 18-item questionnaire assessing LLM chatbot use for work and in daily life, including for health-related purposes, alongside sociodemographic factors. Chi-squared tests were used to assess crude associations and multivariable logistic regression was used to evaluate the adjusted odds ratios of sociodemographic factors and LLM chatbot use. Results: Of 270 respondents (109 online, 161 in person), 119 (44%) reported using LLM chatbots, with 40 respondents (15%) using them for health-related reasons. LLM chatbots were primarily used for general health queries rather than specific or personal health questions. Younger age and a more liberal political view were associated with LLM chatbot use, while gender, education, income, ethnocultural background and language spoken were not. Conclusion: This study showed that a relevant number of individuals with rheumatic diseases are already using LLM chatbots, including for health-related reasons. These findings should prompt urgent efforts to address accuracy, safety and equity concerns regarding the utilization of LLM chatbots, particularly in the domain of rheumatology.
Indexed as
Identifiers
What OpenQuestion holds
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.