患者教育中的大型语言模型:医学应用的范围审查
Serhat Aydin1, Mert Karabacak2, Victoria Vlachos3
1School of Medicine, Koç University, Istanbul, Türkiye.
Frontiers in medicine
|November 13, 2024
概括
大型语言模型 (LLM) 通过生成材料和改善沟通,显示出患者教育的前景. 需要进一步的研究,以确保准确性,并解决可靠的患者参与的伦理问题.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 患者教育 技术 技术
背景情况:
- 大型语言模型 (LLM) 是先进的AI系统,能够处理和生成类似人类的文本.
- 值得注意的LLM包括GPT-4o,克劳德3.5索内特和双子座.
- 将LLM纳入医疗保健提供了提高患者教育和参与度的潜力.
研究的目的:
- 进行范围审查,综合LLM在患者教育和参与中的当前应用.
- 确定与LLM在这个领域的使用相关的新兴主题和挑战.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 在PubMed中使用关键字和MeSH术语对LLM和患者教育进行了2024年6月的搜索.
- 采用专题分析来综合201项已识别的研究的发现.
主要成果:
- 六个主要主题出现了:生成教育材料,解释医疗信息,生活方式建议,药物支持,外科手术期间的护理和医生与患者的互动.
- 在回答查询,增强材料和简化医疗语言方面,LLMs表现出准确性.
- 确定的挑战包括内容可读性,准确性和潜在的偏见.
结论:
- 法律学具有创造可访问的患者教育和改善医疗保健沟通的巨大潜力.
- 进一步的研究对于解决LLM准确性,可读性和安全有效实施的伦理考虑是必不可少的.
- 未来的工作重点应该是完善LLM,确保患者教育中的内容可靠性.
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