通过大型语言模型提高USFDA患者通信的可读性:一项概念验证研究
Kannan Sridharan1, Gowri Sivaramakrishnan2
1Department of Pharmacology & Therapeutics, College of Medicine & Medical Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain.
Expert review of clinical pharmacology
|June 1, 2024
概括
大型语言模型 (LLM) 可以简化复杂的美国食品和药物管理局 (FDA) 药物安全通讯和药物指南. 这些人工智能工具显著提高了可读性,使患者更容易获得关键的健康信息.
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
- 患者教育 患者教育
背景情况:
- 美国食品和药物管理局 (FDA) 的药物安全通讯 (DSC) 和药物指南 (MGs) 提供了复杂的信息.
- 由于这些关键文件的技术性质和阅读水平,患者的理解往往受到挑战.
研究的目的:
- 评估大语言模型 (LLM) 在简化FDA药物安全材料方面的有效性.
- 评估LLM是否可以提高DSC和MG的可读性,以提高平均阅读能力.
主要方法:
- 利用ChatGPT 4.0和Gemini来简化最近的DSC和MG到六年级的阅读水平.
- 评估了LLM生成的内容的可读性,技术准确性和内容完整性.
主要成果:
- 原始材料的阅读成绩水平很高 (DSCs:13,MGs:22).
- 实际上,LLM显著降低了阅读等级水平 (例如,Gemini将MG简化为6.5-6.8).
- 简化内容保持了技术准确性和基本信息.
结论:
- 实际上,LLM有效地提高了患者复杂健康信息的可访问性.
- 建议对不同的语言和现实世界患者群体进行进一步的研究.
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