研究用户为何愿用DeepSeek看病，发现信任是关键中介。
User Intent to Use DeepSeek for Healthcare Purposes and their Trust in the Large Language Model: Multinational Survey Study
- 通过问卷分析易用性、有用性如何经由信任影响使用意愿。
- 信任显著中介易用性对使用意图的影响，风险感知则抑制使用。
- 揭示易用性与风险的非线性关系，适合医疗AI决策者参考。
大型语言模型（LLMs）日益成为交互式医疗资源，但用户接受度仍待深入探讨。本研究调查了易用性、感知有用性、信任与风险感知如何共同影响用户采用基于DeepSeek的LLM平台进行医疗活动的意愿。通过对来自印度、英国和美国的556名参与者进行横断面调查，采用结构方程模型评估直接与间接效应，包括潜在的二次关系。结果表明，信任起关键中介作用：易用性通过信任对使用意愿产生显著间接影响；感知有用性既促进信任形成，也直接推动采纳。相反，风险感知对使用意图具有负面影响，凸显数据治理与透明度的重要性。值得注意的是，易用性与风险感知均呈现显著非线性路径，表明存在阈值或饱和效应。测量模型具备强信度与效度，复合信度、平均方差提取量及判别效度均达标。研究扩展了技术接受与健康信息学领域，揭示敏感场景下用户采纳的多维特征。利益相关方应投资于信任建设、以用户为中心的设计及风险缓解措施，以促进医疗LLM的可持续安全应用。未来可采用纵向设计或考察文化特异性变量，进一步阐明用户感知在时间与不同监管环境下的演变机制。这些洞见对利用AI提升医疗成果至关重要。
原文摘要 · Abstract (English)
Large language models (LLMs) increasingly serve as interactive healthcare resources, yet user acceptance remains underexplored. This study examines how ease of use, perceived usefulness, trust, and risk perception interact to shape intentions to adopt DeepSeek, an emerging LLM-based platform, for healthcare purposes. A cross-sectional survey of 556 participants from India, the United Kingdom, and the United States was conducted to measure perceptions and usage patterns. Structural equation modeling assessed both direct and indirect effects, including potential quadratic relationships. Results revealed that trust plays a pivotal mediating role: ease of use exerts a significant indirect effect on usage intentions through trust, while perceived usefulness contributes to both trust development and direct adoption. By contrast, risk perception negatively affects usage intent, emphasizing the importance of robust data governance and transparency. Notably, significant non-linear paths were observed for ease of use and risk, indicating threshold or plateau effects. The measurement model demonstrated strong reliability and validity, supported by high composite reliabilities, average variance extracted, and discriminant validity measures. These findings extend technology acceptance and health informatics research by illuminating the multifaceted nature of user adoption in sensitive domains. Stakeholders should invest in trust-building strategies, user-centric design, and risk mitigation measures to encourage sustained and safe uptake of LLMs in healthcare. Future work can employ longitudinal designs or examine culture-specific variables to further clarify how user perceptions evolve over time and across different regulatory environments. Such insights are critical for harnessing AI to enhance outcomes.
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