Solmonovich Rachel L, Kouba Insaf, Lee Ji Y, Demertzis Kristen, Blitz Matthew J
Northwell, New Hyde Park, New York, United States of America.
Department of Obstetrics and Gynecology, South Shore University Hospital, Bay Shore, New York; Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States of America.
PLoS One. 2025 May 28;20(5):e0320749. doi: 10.1371/journal.pone.0320749. eCollection 2025.
There is increasing medical interest and research regarding the potential of large language model-based virtual assistants in healthcare. It is important to understand physicians' interest in implementing these tools into clinical practice, so preceding education could be implemented to ensure appropriate and ethical use. We aimed to assess physician 1) awareness of, 2) interest in, and 3) current use of large language model-based virtual assistants for clinical practice and professional development and determine the specific applications of interest and use. Additionally, we wanted to determine associations with age, gender, and role. We conducted a cross-sectional study between 11/08-12/2023 via an anonymous web-based survey that was disseminated among physicians at a large NY healthcare network using snowball sampling. Descriptive and basic inferential statistics were performed. There were 562 respondents, largely males (55.7%), attending physicians (68.5%), and from nonsurgical specialties (67.4%). Most were aware of large language model chatbots (89.7%) and expressed interest (97.2%). Only a minority incorporated it into their practice (21%). Highest levels of interest were for journal review, patient education, and documentation/dictation (88.1-89.5%). The most frequently employed uses were medical information and education and study/research design. Females showed higher interest than males (99.2% vs. 95.5%, p = 0.011). Attendings were more aware of large language models (92.2% vs. 84.2%, p = 0.004), while trainees had increased rates of use (28.8% vs. 17.4%, p = 0.002). Use varied across age brackets, highest among 20-30 year olds (29.1% vs. 13.5%-23.4%, p = 0.018), except for documentation/dictation, where highest use was among the 41-50 year old group (10.5% vs. 2.6%-8.7%, p = 0.047). We concluded that physicians are interested in large language model-based virtual assistants, a minority are implementing it into their practice, and gender-, role-, and age-based disparities exist. As physicians continue to integrate large language models into their patient care and professional development, there is opportunity for research, education, and guidance to ensure an inclusive, responsible, and safe adoption.
对于基于大语言模型的虚拟助手在医疗保健领域的潜力，医学领域的兴趣和研究日益增加。了解医生将这些工具应用于临床实践的兴趣非常重要，因此可以开展前期教育以确保其合理和道德使用。我们旨在评估医生对于基于大语言模型的虚拟助手用于临床实践和专业发展的1）知晓度、2）兴趣以及3）当前使用情况，并确定感兴趣和使用的具体应用。此外，我们还想确定与年龄、性别和角色的关联。我们于2023年11月8日至12月通过一项匿名的基于网络的调查进行了横断面研究，该调查通过雪球抽样在纽约一个大型医疗网络的医生中进行传播。进行了描述性和基本的推断性统计分析。共有562名受访者，其中大部分为男性（55.7%）、主治医生（68.5%），来自非外科专科（67.4%）。大多数人知晓大语言模型聊天机器人（89.7%）并表示感兴趣（97.2%）。只有少数人将其纳入实践（21%）。兴趣最高的方面是期刊评审、患者教育以及文档记录/听写（88.1 - 89.5%）。最常使用的用途是医学信息与教育以及研究/研究设计。女性比男性表现出更高的兴趣（99.2%对95.5%，p = 0.011）。主治医生对大语言模型的知晓度更高（92.2%对84.2%，p = 0.004），而实习生的使用率更高（28.8%对17.4%，p = 0.002）。使用率在不同年龄组有所差异，20 - 30岁年龄组最高（29.1%对13.5% - 23.4%，p = 0.018），除了文档记录/听写方面，41 - 50岁年龄组的使用率最高（10.5%对2.6% - 8.7%，p = 0.047）。我们得出结论，医生对基于大语言模型的虚拟助手感兴趣，少数人将其应用于实践，并且存在基于性别、角色和年龄的差异。随着医生继续将大语言模型整合到患者护理和专业发展中，有机会进行研究、教育和指导，以确保其包容性、负责任和安全的采用。
