Choudhury Avishek, Shahsavar Yeganeh, Shamszare Hamid
Industrial and Management Systems Engineering, Benjamin M Statler College of Engineering and Mineral Resources, West Virginia University, 1306 Evansdale Drive321 Engineering Sciences Building, Morgantown, US.
Industrial and Management Systems Engineering, Benjamin M Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, US.
JMIR Hum Factors. 2025 Apr 7. doi: 10.2196/72867.
BACKGROUND: Generative artificial intelligence (Gen-AI)-particularly large language models (LLMs)-has generated unprecedented interest in applications ranging from everyday Q&A to health-related inquiries. However, little is known about how everyday users decide whether to trust and adopt these technologies-particularly in high-stakes contexts like personal health. OBJECTIVE: This study examines how ease of use, perceived usefulness, and risk perception interact to shape user trust in and intentions to adopt DeepSeek, an emerging LLM-based platform, for healthcare purposes. METHODS: We adapted survey items from validated technology acceptance scales to assess user perception of DeepSeek, focusing on constructs such as trust, intent to use for health, ease of use, perceived usefulness, and risk perception. A 12-item Likert scale questionnaire was developed and pilot-tested (n=20) for clarity and consistency. It was then distributed online to users in India (IND), United Kingdom (UK), and United States of America (USA) who had used DeepSeek within the past two weeks. Data analysis involved descriptive frequency assessments and Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement and structural models. Structural equation modeling assessed direct and indirect effects, including potential quadratic relationships. RESULTS: A total of 556 complete responses were collected, with respondents almost evenly split across IND (n=184), the UK (n=185), and the USA (n=187). Regarding AI in healthcare, when asked if they were comfortable with their healthcare provider using AI tools, 59.3% (n=330) were fine with AI use provided their doctor verified its output, and 31.5% (n=175) were enthusiastic about its use without conditions. DeepSeek was used primarily for academic and educational purposes, 50.7% (n=282) used DeepSeek as a search engine, and 47.7% (n=265) for health-related queries. When asked about their intent to adopt DeepSeek over other LLMs like ChatGPT, 52.1% (n=290) were likely to switch, and 28.9% (n=161) were very likely to do so. The study revealed that trust plays a pivotal mediating role: ease of use exerts a significant indirect impact on usage intentions through trust. At the same time, perceived usefulness contributes to trust development and direct adoption. By contrast, risk perception negatively affects usage intent, emphasizing the importance of robust data governance and transparency. Significant non-linear paths were observed for ease of use and risk, indicating threshold or plateau effects. CONCLUSIONS: Users are receptive to DeepSeek when it's easy to use, useful, and trustworthy. The model highlights trust as a mediator and shows non-linear dynamics shaping AI-driven healthcare tool adoption. Expanding the model with mediators like privacy and cultural differences could provide deeper insights. Longitudinal or experimental designs could establish causality and track user attitudes. Further investigation into threshold and plateau phenomena could refine our understanding of user perceptions as they become more familiar with AI-driven healthcare tools.
背景：生成式人工智能（Gen-AI），尤其是大语言模型（LLMs），在从日常问答到健康相关咨询等广泛应用领域引发了前所未有的关注。然而，对于普通用户如何决定是否信任并采用这些技术，尤其是在个人健康这种高风险情境下，我们所知甚少。 目的：本研究探讨易用性、感知有用性和风险感知如何相互作用，以塑造用户对新兴的基于大语言模型的平台通义千问（DeepSeek）用于医疗保健目的的信任和采用意愿。 方法：我们改编了经过验证的技术接受量表中的调查项目，以评估用户对通义千问的看法，重点关注信任、用于健康领域的使用意愿、易用性、感知有用性和风险感知等构念。编制了一份包含12个条目的李克特量表问卷，并进行了预测试（n = 20）以确保清晰度和一致性。然后将问卷在线分发给过去两周内使用过通义千问的印度（IND）、英国（UK）和美国（USA）的用户。数据分析包括描述性频率评估和偏最小二乘结构方程模型（PLS-SEM），以评估测量模型和结构模型。结构方程模型评估直接和间接效应，包括潜在的二次关系。 结果：共收集到556份完整回复，受访者在印度（n = 184）、英国（n = 185）和美国（n = 187）之间分布几乎均匀。关于医疗保健中的人工智能，当被问及是否愿意让其医疗服务提供者使用人工智能工具时，59.3%（n = 330）的人表示只要医生核实其输出结果就接受使用人工智能，31.5%（n = 175）的人无条件地热衷于使用人工智能。通义千问主要用于学术和教育目的，50.7%（n = 282）的人将通义千问用作搜索引擎，47.7%（n = 265）的人用于健康相关查询。当被问及与ChatGPT等其他大语言模型相比采用通义千问的意愿时，52.1%（n = 290）的人可能会切换，28.9%（n = 161）的人非常可能会切换。研究表明，信任起着关键的中介作用：易用性通过信任对使用意愿产生显著的间接影响。同时，感知有用性有助于信任的发展和直接采用。相比之下，风险感知对使用意愿产生负面影响，强调了强大的数据治理和透明度的重要性。观察到易用性和风险存在显著的非线性路径，表明存在阈值或平台效应。 结论：当通义千问易于使用、有用且值得信赖时，用户会接受它。该模型突出了信任作为中介的作用，并显示了塑造人工智能驱动的医疗保健工具采用的非线性动态。用隐私和文化差异等中介因素扩展该模型可以提供更深入的见解。纵向或实验设计可以确定因果关系并跟踪用户态度。对阈值和平台现象的进一步研究可以完善我们对用户在更熟悉人工智能驱动的医疗保健工具时的认知的理解。
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