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对生物医学中的大型语言模型进行调查

Chong Wang¹, Mengyao Li², Junjun He³

¹School of Medical Engineering, Henan Medical University, Xinxiang, China; Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China; Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China.

Artificial intelligence in medicine

|September 25, 2025

概括

大型语言模型 (LLM) 在生物医学中显示出对诊断和药物发现等任务的希望. 本综述分析了它们的应用,数据隐私等挑战,以及医疗保健中负责任的人工智能的未来方向.

科学领域:

人工智能的人工智能
生物医学信息学生物医学信息学
自然语言处理自然语言处理.

背景情况:

大型语言模型 (LLM) 展示了自然语言理解和生成的先进能力.
现有的生物医学LLM调查往往缺乏对各种应用和最近的进展进行全面分析.
本综述侧重于LLM在现实世界生物医学环境中的实际含义.

研究的目的:

提供对当前生物医学LLM的现状,应用,挑战和前景的深入研究.
分析专业生物医学任务的零射击学习和适应策略中的LLM能力.
确定未来的研究方向,以在医疗保健中负责和有效地部署LLM.

主要方法:

在PubMed,Web of Science和arXiv的484篇出版物的系统审查.
在诊断辅助,药物发现和个性化医学 (137项研究) 等领域对LLM应用的分析.
审查LLM适应策略,包括对单模和多模模型的微调.

主要成果:

在各种生物医学任务中,LLM在零射击学习方面表现出显著的潜力.
微调策略对于提高LLM在专业领域的表现至关重要,例如医疗问题答案.
关键的挑战包括数据隐私,模型可解释性,数据集质量和道德考虑.

结论:

在生物医学中,LLM提供了变革性的潜力,但必须应对挑战,以确保安全有效的实施.
未来的研究应该专注于保护隐私的方法,如联合学习和可解释的人工智能.
持续发展对于在医疗保健中负责任地利用LLM能力至关重要.

关键词:

生物医学生物医学微调策略的微调策略.大型语言模型.

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