Wang Ellen, Smith Justin, Katz Steven, Bishay Mena, Dissanayake Tharindri, Jones Niall, Reddy Saurash, Sholter Dalton, Soo Jason, Ye Carrie
Arthritis Consumer Experts, Vancouver, BC, Canada.
Faculty of Medicine, Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada.
Rheumatol Adv Pract. 2025 Jul 12;9(3):rkaf083. doi: 10.1093/rap/rkaf083. eCollection 2025.
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.
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.
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.
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.
人工智能（AI）技术，尤其是基于大语言模型（LLM）的聊天机器人的迅速主流化，引发了人们对其在医疗保健中潜在作用的兴趣。尽管技术有所进步，但对于患有风湿性疾病的个体目前对LLM聊天机器人的使用情况知之甚少。本研究旨在调查患有风湿性疾病的个体对LLM聊天机器人的采用情况和看法，以及相关的社会人口学因素。
通过关节炎护理专家的数字和社交媒体平台在线招募参与者，并从加拿大艾伯塔省埃德蒙顿的风湿病诊所亲自招募参与者，进行了一项探索性横断面调查。受访者完成了一份包含18个条目的问卷，评估LLM聊天机器人在工作和日常生活中的使用情况，包括用于健康相关目的的情况，以及社会人口学因素。使用卡方检验评估粗略关联，并使用多变量逻辑回归评估社会人口学因素与LLM聊天机器人使用的调整后优势比。
在270名受访者（109名在线，161名亲自参与）中，119名（44%）报告使用了LLM聊天机器人，其中40名受访者（15%）因健康相关原因使用它们。LLM聊天机器人主要用于一般健康问题查询，而非特定或个人健康问题。年龄较小和政治观点较为开放与LLM聊天机器人的使用相关，而性别、教育程度、收入、种族文化背景和所讲语言则无关。
本研究表明，相当数量的风湿性疾病患者已经在使用LLM聊天机器人，包括出于健康相关原因。这些发现应促使人们迫切努力解决与LLM聊天机器人使用相关的准确性、安全性和公平性问题，尤其是在风湿病领域。
