大型语言模型在回答免疫瘤学问题的比较:一个横截面研究
Giovanni Maria Iannantuono1, Dara Bracken-Clarke2, Fatima Karzai1
1Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
The oncologist
|February 3, 2024
概括
聊天GPT-4和聊天GPT-3.5显示作为免疫瘤学工具的承诺,超过了谷歌Bard. 然而,所有大型语言模型 (LLM) 由于潜在的不准确性,需要专家验证.
科学领域:
- 在瘤学中使用人工智能
- 在医疗保健中的自然语言处理.
背景情况:
- 大型语言模型 (LLM) 为癌症患者和医疗保健提供者提供了教育和管理工具的潜力.
- 在免疫瘤学等专业医疗领域调查LLM能力至关重要.
研究的目的:
- 评估ChatGPT-4,ChatGPT-3.5和Google Bard在回答免疫瘤学问题的表现.
- 为了比较不同LLM的答案的准确性,相关性和可重复性.
主要方法:
- 一项横截面研究在四个领域产生了60个开放式免疫瘤学问题.
- 问题提交给了ChatGPT-4,ChatGPT-3.5和谷歌巴德.
- 两位评论员独立评估了答案的准确性,相关性和可读性.
主要成果:
- 聊天GPT-4和聊天GPT-3.5回答了所有问题,而谷歌巴德回答了53.3%.
- 与谷歌Bard相比,ChatGPT模型的准确性 (75.4%的GPT-4,58.5%的GPT-3.5) 和可重复性 (95%的GPT-4,88.3%的GPT-3.5) 较高.
- 对于ChatGPT模型 (98.1%-100%) 和谷歌巴德 (87.5%) 的可读性很高.
结论:
- 聊天GPT-4和聊天GPT-3.5显示出作为免疫瘤学工具的巨大潜力.
- 谷歌Bard在评估的指标中表现较差.
- 专家验证对于免疫瘤学中所有LLM产生的内容至关重要,因为存在不准确的风险.
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