目的：探讨DeepSeek在医学科研管理中的应用效果、风险及对策。方法：采用文献分析与案例测试法，模拟项目管理、计划制定等五大场景共十个问题，评估DeepSeek的响应质量。结果：DeepSeek在项目规划、文书撰写等结构化任务中效率高、参考性强；但在实时数据检索、本土政策匹配及复杂伦理判断上存在信息滞后与“AI幻觉”风险，且引发隐私泄露与学术诚信争议。结论：DeepSeek是高效的辅助工具而非决策主体。核心发现包括：其应用存在显著的场景依赖性；需警惕数据安全风险与人员能力退化；必须建立“人机协同”复核机制。建议医疗机构完善AI使用伦理规范，推行使用备案与声明制度，加强管理人员数智素养培训，以确保技术应用安全、合规、高效。

Objective：To explore the effectiveness，risks，and countermeasures of DeepSeek in medical scientific research management. Methods：Using literature analysis and case testing，ten questions across five scenarios-such as project management and plan formulation-were simulated to evaluate DeepSeek’s response quality. Results：DeepSeek demonstrates high efficiency and strong reference value in structured tasks like project planning and document drafting. However，it faces risks of information lag and “AI hallucination” in real-time data retrieval，local policy alignment，and complex ethical judgment，while also raising concerns about privacy leaks and academic integrity disputes. Conclusion：DeepSeek serves as an efficient auxiliary tool rather than a decision-making entity. Key findings include its significant scenario dependency，the need to guard against data security risks and skill degradation in personnel，and the imperative to establish a “human-machine collaboration” review mechanism. It is recommended that medical institutions improve AI usage ethics standards，implement registration and declaration systems，enhance digital literacy training for management personnel，and ensure safe，compliant，and efficient technological application.