在制药计算中评估大型语言模型的准确性:聊天GPT和MathGPT的比较
Bernadette Cornelison1, Christopher Edwards1, Crystal Zhang1
1University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ, USA.
像ChatGPT和MathGPT这样的生成人工智能工具在制药计算中显示了70%的准确性,这对于单独使用是不够的. 药房教育工作者必须强调基础技能和关键AI响应评估.
科学领域:
- 药房 教育 教育 药房 教育
- 医疗保健中的人工智能
- 计算化学计算化学
背景情况:
- 生成型人工智能,包括像ChatGPT和MathGPT这样的大型语言模型 (LLM),越来越多地用于教育.
- 士课程为学生提供快速访问药品计算的答案,引发了对关键技能准确性的担忧.
研究的目的:
- 评估ChatGPT 3.5和MathGPT Unlimited在解决制药计算问题的准确性.
- 评估这些人工智能工具的性能与学生药剂师课程相比.
主要方法:
- 从药房课程中选择了50个药品计算问题,与北美药剂师执照考试主题保持一致.
- 来自ChatGPT 3.5和MathGPT Unlimited的答案与由教师验证的答案密钥进行了比较.
主要成果:
- 聊天GPT和MathGPT都达到了70%的准确度,正确回答了50个问题中的35个,两者之间没有显著差异.
- 精度因主题而异,在化等效和溶液中性能较低.
- 常见的错误包括单位转换,计算方法的错误应用和精度问题.
结论:
- 对于ChatGPT 3.5和MathGPT Unlimited来说,70%的准确率不足以用于药品计算中的专用用途.
- 药房教育工作者应该优先考虑基础计算技能,并教学生批判性地评估人工智能生成的答案.
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