BEIJING/SINGAPORE - Chinese state-owned defense giant Norinco in February unveiled a military vehicle capable of autonomously conducting combat-support operations at 50 kilometers per hour. It was powered by DeepSeek, the company whose artificial intelligence model is the pride of China's tech sector. The Norinco P60's release was touted by Communist Party officials in press statements as an early showcase of how Beijing is using DeepSeek and AI to catch up in its arms race with the United States, at a time when leaders in both countries have urged their militaries to prepare for conflict. A review of hundreds of research papers, patents and procurement records gives a snapshot of the systematic effort by Beijing to harness AI for military advantage. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

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.