Background The open-source of the large language model (LLM) DeepSeek-R1 makes local deployment by medical institutions possible, while the characteristics of these models and hospitals are still unexplored. Methods We collected open-source data from January 1 to March 8, 2025, covering both public and private hospitals deploying DeepSeek-R1 in China. This nationwide cross-sectional survey covered hospital characteristics (institution grade and geographical locations), and model parameters and functional applications of deployed models. Results In total 261 hospitals We found that DeepSeek-R1 was rapidly adopted across 93.5% of Chinese provinces, with tertiary hospitals accounting for 84% of deployments. However, geographical disparities persist, with more pilot projects in the Central South, East China, and North China regions. Functionally, DeepSeek supports various medical scenarios, including patient services, clinical diagnosis, and hospital management. The 671B model outperforms the 70B and 32B models, especially in clinical decision support and medical record generation. Interpretation The study concluded that while DeepSeek's deployment was fast, disparities existed based on geography and hospital level. Higher-parameter models offer superior functionality, providing insights into medical institutions' overall LLM deployment trend. Funding: The study was supported by the National Social Science Fund of China (23BGL249). Keywords: artificial intelligence; large language model; local deployment; hospital information system; intelligent healthcare

Mian-mian Yao is the employee of Tigermed Co., Ltd (a clinical research organization). The other authors declare no competing interests.

The study was supported by the National Social Science Fund of China (23BGL249).

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