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在多发性硬化症管理方面,将大型语言模型整合到护理,研究和教育中

Hernan Inojosa¹, Isabel Voigt¹, Judith Wenk¹

¹Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus Dresden, Technical University Dresden, Dresden, Germany.

Multiple sclerosis (Houndmills, Basingstoke, England)

|September 23, 2024

概括

大型语言模型 (LLM) 显示出改变多发性硬化症 (MS) 护理,从诊断到患者教育的巨大前景. 进一步的研究和人类监督对于它们在MS管理中的负责任实施至关重要.

科学领域:

人工智能的人工智能
神经学神经学
医疗信息学医疗信息学

背景情况:

生成型人工智能 (AI),特别是大型语言模型 (LLM),在文本生成和理解方面表现出先进的能力.
虽然人工智能在多发性硬化症 (MS) 的医学成像和疾病预后方面正在获得吸引力,但在MS管理中LLM应用的探索较少.
在MS护理的各个方面,LLM提供了潜在的好处,包括临床决策支持和患者教育.

研究的目的:

审查LLMs在多发性硬化症 (MS) 管理中的实际应用.
为了确定LLMs在MS护理中的潜在未来用途.
考虑监管方面的挑战,以及在MS的LLM部署中对人类监督的必要性.

主要方法:

对当前文献和LLMs在MS的潜在应用进行了专注的审查.
探索临床决策支持,数据分析和教育中的LLM能力.
讨论监管方面的考虑和人类监督的作用.

主要成果:

在MS中,LLM可能会增强对疾病修饰疗法的临床决策.
由LLM驱动的AI工具可以利用非结构化的真实世界数据进行MS研究.
基于LLM的虚拟导师可以为医疗保健专业人员和MS患者提供适应性的教育资源.

结论:

在多发性硬化症管理方面,LLM具有变革性的潜力,涵盖诊断,治疗,研究和教育.
解决监管障碍和确保人类监督对于有效和安全地将LLM纳入MS护理至关重要.
这一综述是未来对MS连续性内的LLM应用研究的基础.

关键词:

多发性硬化症是多发性硬化症.应用程序应用程序应用程序人工智能的人工智能是人工智能.疾病管理疾病管理大型语言模型 (LLM)

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