精度和个性化:大型语言模型如何重新定义个性化医学的诊断准确性 - - 系统性文献综述
IEEE journal of biomedical and health informatics
|June 30, 2025
概括
大型语言模型 (LLM) 正在提高个性化医学的诊断准确性. 这篇综述表明,尽管存在挑战,LLMs发现了更好的患者特定治疗的微妙模式.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 个性化医疗是个性化的医疗.
背景情况:
- 个性化医疗需要高度的诊断准确性,以解决疾病的个体变化.
- 大型语言模型 (LLM) 为数据分析和模式识别提供了先进的功能.
研究的目的:
- 系统地审查LLMs在提高个性化医学的诊断精度方面的作用.
- 分析LLM在这一领域的应用,性能和挑战.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 搜索了科学网,科学直播,Scopus和IEEE Xplore (2020年1月至2025年3月).
- 分析了39篇关于个性化医学LLM的同行评审文章.
主要成果:
- 法学士越来越多地融入临床信息学,医学成像和决策支持领域.
- LLM在识别细微模式方面具有潜力,可用于准确的诊断和治疗规划.
- 确定的挑战包括可概括性,数据集偏差,数据隐私,可解释性和可靠性.
结论:
- 在提高诊断准确性和推进个性化医疗方面,LLM显著有前途.
- 解决伦理标准,模型验证和可解释性对于临床整合至关重要.
- 未来的研究应该专注于为负责任的医疗保健应用改进LLM技术.
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