在以患者为中心的药物指导和自决支持中,导航大型语言模型的潜力和陷
Serhat Aydin1, Mert Karabacak2, Victoria Vlachos3
1School of Medicine, Koç University, Istanbul, Türkiye.
Frontiers in medicine
|February 7, 2025
概括
大型语言模型 (LLM) 为患者提供可访问的药物信息,但存在错误信息的风险. 这些人工智能工具应该补充,而不是取代专业医疗保健指南,以安全管理药物.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者教育 患者教育
背景情况:
- 大型语言模型 (LLM) 越来越多地用于患者教育.
- 这种观点建立在对患者教育中的LLMs的范围审查之上.
- 重点是LLMs在药物指导中的具体作用.
研究的目的:
- 检查LLMs在药物指导中的作用.
- 分析LLM在这个领域的当前能力和局限性.
- 提出有关LLM在药物管理中的整合问题的问题.
主要方法:
- 这是一篇分析现有文献和AI能力的观点文章.
- 讨论潜在的好处和风险.
- 确定未来需要考虑的挑战和领域.
主要成果:
- 临床医学仪器可以产生全面的药物信息,潜在地增强患者的自主权.
- 显著的风险包括错误信息和无法访问个体患者数据.
- 当患者仅仅依赖人工智能做出自我治疗决策时,就会出现安全问题.
结论:
- 在药物指导方面,LLM显示出潜力,但存在关键局限性.
- 监管监督对于确保LLM补充专业医疗保健至关重要.
- 强调使用LLM作为辅助,而不是替代医疗保健建议.
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