了解医疗保健中的大型语言模型:临床实施和解释出版物的指南
Julia Maslinski1,2, Rachel Grasfield3,1, Raghav Awasthi4,1
1Artificial Intelligence, BrainXAI ReSearch, BrainX LLC, Cleveland, USA.
Cureus
|May 19, 2025
概括
大型语言模型 (LLM) 在医疗保健领域具有重大潜力. 本综述阐明了LLM术语,评估方法和应用程序,以帮助医疗保健专业人员了解这项AI技术.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 大型语言模型 (LLM) 正在快速发展,显示出改变医疗保健服务的潜力.
- 越来越多的LLM研究需要对医疗保健专业人员的术语和应用有明确的理解.
- 弥合LLM能力的知识差距对于有效的临床和行政采用至关重要.
研究的目的:
- 为医疗保健领域的LLM的发展和演变提供全面的概述.
- 定义与LLM相关的关键术语,增强医疗保健专业人员的理解力.
- 探索评估方法和LLMs在医疗保健环境中的实际应用.
主要方法:
- 文献综述,重点关注LLM在医疗保健中的演变和应用.
- 对医学专业人员相关的核心LLM术语和概念的系统描述.
- 在LLM研究中使用的实验和研究方法的分析.
- 在临床和行政医疗保健场景中LLM应用的说明性示例.
主要成果:
- 通过先进的人工智能,LLM为改善医疗保健提供提供了多种机会.
- 了解LLM术语和评估指标对于临床医生来说至关重要.
- 成功采用需要考虑患者和医疗保健专业人士的观点.
结论:
- 本综述为医疗保健专业人员提供了对LLM的基础知识,用于研究和实践.
- 消除LLM的神秘性有助于对LLM融入医疗保健系统做出明智的决策.
- 终身医疗器械 (LLM) 代表了一项重要的技术进步,有可能提高患者护理和运营效率.
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