大型语言模型关于抗体-药物结合物和相关的眼部不良影响的准确性和完整性
Rayna Marshall1, Hannah Xu2, Lauren A Dalvin3
1The Drexel University College of Medicine, Philadelphia, PA.
Cornea
|August 7, 2024
概括
像ChatGPT和Bard这样的大型语言模型 (LLM) 在生成抗体-药物合物 (ADC) 相关的眼部毒性信息方面表现出高准确性和完整性,为眼科提供可靠的见解.
科学领域:
- 眼科医生 眼科 眼科
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 抗体-药物合物 (ADCs) 是一种快速发展的向癌症疗法.
- 对于某些ADC来说,眼部毒性是一个重大问题,需要专门的知识来管理.
- 大型语言模型 (LLM) 在复杂的医学领域提供了信息检索和合成的潜力.
研究的目的:
- 评估三个领先的LLM (ChatGPT,Bard,LLaMA) 在提供关于ADC相关眼部毒性的信息时的准确性和完整性.
- 为了比较这些LLM在生成与tisotumab vedotin和mirvetuximab soravtansine等特定ADC相关的数据方面的表现.
主要方法:
- 对ChatGPT,Bard和LLaMA提出了22个专家开发的关于ADC和眼睛毒性的问题.
- 眼睛毒性专家使用6分利克特尺度对LLM生成的答案进行了准确性和完整性的评分.
- 使用包括ANOVA和t测试在内的统计分析来比较LLM的表现,Fleiss kappa评估了interrater可靠性.
主要成果:
- 这三个LLM都获得了高平均准确性和完整性得分.
- 聊天GPT (4.62) 和Bard (4.77) 的准确性明显高于LLaMA (4.41).
- 在LLM中没有观察到完整性得分的显著差异;评审者之间的可靠性对准确性 (0.74) 很好,对完整性 (0.31) 相当.
结论:
- 法律学士可以提供足够准确和完整的有关眼科专业主题的信息,如ADC相关的眼部毒性.
- 与LLaMA相比,ChatGPT和Bard似乎在这个领域提供了略高的精度.
- 随着ADC毒性的研究和治疗的发展,建议对LLM进行持续的重新评估.
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