Adoption and perception of LLM-based chatbots in health care: an exploratory cross-sectional survey of individuals with rheumatic diseases.

IF 2.1 Q3 RHEUMATOLOGY
Rheumatology Advances in Practice Pub Date : 2025-07-12 eCollection Date: 2025-01-01 DOI:10.1093/rap/rkaf083
Ellen Wang, Justin Smith, Steven Katz, Mena Bishay, Tharindri Dissanayake, Niall Jones, Saurash Reddy, Dalton Sholter, Jason Soo, Carrie Ye
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引用次数: 0

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.

在医疗保健中采用和感知基于法学硕士的聊天机器人:对患有风湿性疾病的个体的探索性横断面调查。
人工智能(AI)技术,特别是基于大语言模型(LLM)的聊天机器人的快速主流应用,引发了人们对其在医疗保健领域潜在作用的兴趣。尽管技术进步,但人们对目前LLM聊天机器人在风湿病患者中的应用知之甚少。本研究旨在调查风湿病患者对LLM聊天机器人的采用和认知,以及相关的社会人口因素。方法:进行了一项探索性横断面调查,参与者通过关节炎护理专家的数字和社交媒体平台在线招募,并从加拿大埃德蒙顿的风湿病诊所亲自招募。受访者完成了一份18项调查问卷,评估法学硕士聊天机器人在工作和日常生活中的使用情况,包括与健康相关的目的,以及社会人口因素。采用卡方检验评估粗相关性,采用多变量logistic回归评估社会人口因素与LLM聊天机器人使用的校正比值比。结果:在270名受访者中(109名在线,161名亲自),119名(44%)表示使用LLM聊天机器人,40名受访者(15%)出于健康原因使用它们。LLM聊天机器人主要用于一般健康问题,而不是特定或个人健康问题。年轻的年龄和更自由的政治观点与法学硕士聊天机器人的使用有关,而性别、教育程度、收入、种族文化背景和使用的语言则无关。结论:这项研究表明,相关数量的风湿病患者已经在使用LLM聊天机器人,包括出于与健康相关的原因。这些发现应该促使人们立即努力解决LLM聊天机器人使用的准确性、安全性和公平性问题,特别是在风湿病学领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Rheumatology Advances in Practice
Rheumatology Advances in Practice Medicine-Rheumatology
CiteScore
3.60
自引率
3.20%
发文量
197
审稿时长
11 weeks
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