在医疗保健中促进负责任的深度搜索部署:范围审查与灰色和白色文献进行比较
Wang Jiang1, Dan Wang1, Yihang Zeng2
1School of Management, Hubei University of Chinese Medicine, Wuhan, China.
开源大型语言模型 (LLM) DeepSeek 在中国医院迅速被采用,用于临床决策支持. 然而,部署缺乏透明度和严格的风险评估,可能会影响患者的安全.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 医疗技术评估 医疗技术评估
背景情况:
- 开源大型语言模型 (LLM) DeepSeek 在医疗保健领域得到了快速采用,引发了人们对患者安全和结果的担忧.
- 关于在临床环境中管理DeepSeek的特定使用和监管框架的信息有限.
研究的目的:
- 系统地审查DeepSeek在中国100家顶级医院的部署特点.
- 根据医院披露的数据,将DeepSeek报告的性能和风险与现有研究证据进行比较.
主要方法:
- 在医院网站/社交媒体和学术数据库 (Web of Science,PubMed) 搜索了2025年1月15日之后的DeepSeek部署和研究.
- 使用开发的编码框架提取了关于DeepSeek特征,应用,评估,风险和法规的数据.
- 使用乔安娜·布里格斯研究所的检查清单评估了偏见风险.
主要成果:
- 58个DeepSeek模型在中国前100家医院中的48家被确定;发现了27个相关研究.
- DeepSeek-reasoner (R1) 是最常见的模型,主要用于临床决策支持 (诊断,治疗).
- 医院披露的信息显示,对预部署评估 (36%),结果 (22%) 和风险管理 (9%) 的透明度很差.
结论:
- 在中国领先的医院中,DeepSeek的部署迅速且广泛,主要用于临床决策支持.
- 医院披露的信息缺乏透明度和严格验证,对患者安全构成潜在风险.
- 迫切需要扩大法规和更透明的报告政策,以便在医疗保健中负责任地使用LLM.
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