评估大型语言模型在推进医疗保健教育方面的当前局限性
JaeYong Kim1, Bathri Narayan Vajravelu2
1School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences, Boston, MA, United States.
JMIR formative research
|January 17, 2025
概括
大型语言模型 (LLM) 在医疗保健教育中提供了变革的潜力,但也带来了重大挑战. 解决错误信息,偏见和监管差距等问题对于安全有效的整合至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 医疗教育 技术 技术 医学教育
背景情况:
- 大型语言模型 (LLM),包括生成预训练变压器,越来越多地被整合到医疗保健中.
- 这种整合对教育和临床管理都有变革的潜力.
- 然而,它们的采用也引发了重大担忧,需要仔细评估.
研究的目的:
- 评估最新的法学士在医疗保健教育中的应用.
- 识别和突出需要紧急改进的关键缺陷.
- 为未来的研究方向提供信息,以有效实施LLM.
主要方法:
- 对当前在医疗保健教育中的LLM应用进行定性分析.
- 识别和分类与LLM使用相关的主要挑战和风险.
- 文学综述和专家意见综合对LLM的局限性.
主要成果:
- 确定了需要改进的八个关键领域:学术完整性的威胁,错误信息,自动化偏见,信息差距,访问不平等,算法偏见,道德不稳定性和技术/监管缺陷.
- 强调迫切需要采取战略干预措施来缓解这些风险.
- 强调了当前插件工具和监管框架的局限性.
结论:
- 在医疗保健教育的LLM整合需要仔细考虑发现的缺陷.
- 未来的研究必须专注于制定应对这些挑战的战略.
- 需要采取有效措施,以改善在医疗保健教育中安全和公平地应用LLMs.
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