临床试验中的大型语言模型:应用,技术进步和未来方向
Anqi Lin1, Zhihan Wang1, Aimin Jiang2
1Donghai County People's Hospital (Affiliated Kangda College of Nanjing Medical University); Department of Oncology, Zhujiang Hospital, Southern Medical University, Lianyungang, Jiangsu, 222000, China.
BMC medicine
|October 15, 2025
概括
大型语言模型 (LLM) 为临床试验挑战提供解决方案,如招聘和数据管理. 本综述探讨了临床研究中的LLM应用,好处和实施障碍.
科学领域:
- 临床研究信息学 临床研究信息学
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
背景情况:
- 临床试验面临越来越多的复杂性,导致参与者招募,数据管理和风险监测方面的挑战.
- 这些问题可能会损害试验的可靠性和安全性,并增加失败的风险.
- 大型语言模型 (LLM) 显示出通过先进的自然语言处理 (NLP) 能力来解决这些挑战的希望.
研究的目的:
- 审查LLM在临床试验设计和进行中的应用.
- 强调LLMs在临床试验工作流程中的现实世界的整合.
- 讨论LLM实施的比较优势,技术限制和未来的挑战.
主要方法:
- 针对临床试验任务的LLMs的领域特定预培训和微调.
- 在自动化患者试验匹配中探索LLM应用.
- 对LLM数据提取,处理和为临床试验提供洞察力的能力进行分析.
主要成果:
- 临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床
- 法律学可以提取和处理临床试验数据,为科学理由和决策提供见解.
- 研究越来越多地探索LLM整合到临床试验设计和执行中.
结论:
- 临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床临床
- 与传统的NLP模型相比,比较优势是显而易见的,与现有的技术局限性相结合.
- 解决实施挑战对于LLMs在临床研究中的未来成功至关重要.
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