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.

翻译：大型语言模型（LLMs）日益成为交互式医疗保健资源，但用户接受度仍待深入探究。本研究探讨了易用性、感知有用性、信任和风险感知如何相互作用，从而影响用户采用新兴基于LLM的平台DeepSeek进行医疗保健的意图。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，测量了其感知和使用模式。采用结构方程模型评估了直接和间接效应，包括潜在的二次关系。结果显示，信任起着关键的中介作用：易用性通过信任对使用意图产生显著的间接影响，而感知有用性既促进信任发展，也直接推动采用。相比之下，风险感知对使用意图产生负面影响，突显了健全的数据治理和透明度的重要性。值得注意的是，易用性和风险感知存在显著的非线性路径，表明存在阈值或平台效应。测量模型表现出较强的信度和效度，得到了较高的组合信度、平均方差提取率和判别效度指标的支持。这些发现通过阐明敏感领域中用户采用的多维性质，扩展了技术接受和健康信息学研究。利益相关者应投资于信任建立策略、以用户为中心的设计和风险缓解措施，以促进LLMs在医疗保健领域的持续和安全应用。未来工作可采用纵向设计或考察文化特定变量，以进一步阐明用户感知如何随时间推移及在不同监管环境中演变。此类见解对于利用人工智能改善医疗结果至关重要。