超越多选准确度:在医疗保健中实施大型语言模型的现实世界挑战
Yifan Yang1, Qiao Jin1, Qingqing Zhu1
1Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA;
Annual review of biomedical data science
|April 8, 2025
概括
大型语言模型 (LLM) 在医疗保健方面表现有前途,但面临着重大的运营,道德,绩效和法律挑战. 克服这些障碍对于它们在医疗应用中负责任地整合至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 大型语言模型 (LLM) 展示了人类水平的能力,推动了医疗保健领域的探索.
- 在各种医疗应用中,LLM具有显著的潜力.
研究的目的:
- 识别和讨论阻碍医学LLM在现实世界中实施的关键挑战.
- 从多个关键角度提供障碍的全面概述.
主要方法:
- 通过四个不同的镜头分析挑战:运营漏洞,道德/社会考虑,绩效/评估以及法律/监管合规性.
- 文献综述和专家讨论目前在医疗保健中的LLM限制.
主要成果:
- 确定了影响医疗环境中LLM可靠性的操作漏洞.
- 突出了对患者信任和公平至关重要的伦理和社会考虑.
- 评估医学LLM准确性的详细表现和评估困难.
- 概述了安全部署的法律和监管合规问题.
结论:
- 解决这些多方面的挑战对于释放LLM在医疗保健中的全部潜力至关重要.
- 负责任的整合需要克服技术,道德和监管障碍.
- 未来的研究应该专注于医疗LLM部署的强有力的解决方案.
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