探索像ChatGPT这样的LLM在药学教育中的作用,以支持学生的治疗决策
Paola Carou-Senra1, Irene Delgado-Taboada1, Carmen Alvarez-Lorenzo1
1Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Materiales (iMATUS) and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain.
大型语言模型对培训药学学生进行药物剂量调整充满希望. 虽然ChatGPT表现最好,但提示设计显著影响准确性,表明潜力,但需要进一步研究.
科学领域:
- 药理学 药理学是指药理学的学科.
- 医疗保健中的人工智能
- 药房 教育 教育 药房 教育
背景情况:
- 临床学生药剂师需要在药物剂量调整方面进行强有力的培训.
- 大规模语言模型 (LLM) 为教育支持提供了新的机会.
研究的目的:
- 评估LLM (ChatGPT,Gemini,Copilot) 作为临床学生药剂师培训工具的有效性.
- 评估LLM在药物剂量调整计算中的实用性,并确定限制.
主要方法:
- 三种LLM在涉及塔克罗利斯,万科米辛和利多卡因的药理学问题上进行了测试.
- 模型性能在每种药物的三个场景中进行了评估,随着结构的快速变化.
- 使用数值尺度 (0-2) 评价模型性能.
主要成果:
- 聊天GPT展示了最高的性能和准确性.
- 由于更简单的计算,塔克罗利斯的剂量计算产生了更正确的答案.
- 快速结构修改显著影响了模型输出,强调了快速工程.
结论:
- 尽管目前的局限性,LLM显示出作为药学学生的支持工具的潜力.
- 进一步的研究,提高数据质量和优化快速设计对于有效实施至关重要.
- 对于安全和有益的医疗保健应用,教育用户关于LLM能力和局限性至关重要.
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