人工智能生成了临床分数表:看看Janus的两个面孔
1Animal Health and Welfare Division, Federal Food Safety and Veterinary Office, Bern, Switzerland. cristian.berce@blv.admin.ch.
Laboratory animal research
|May 15, 2024
概括
大型语言模型 (LLM) 可以为临床前研究起草动物福利分数表,但需要专家审查症状严重程度和干预指南的准确性.
科学领域:
- 临床前研究 临床前研究
- 动物福利科学 动物福利科学
- 研究中的人工智能.
背景情况:
- 临床分数表对于客观的动物福利评估至关重要.
- 人工智能 (AI) 正在改变临床前研究文档.
- 标准化得分表对于尽量减少动物痛苦至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 用于生成动物福利分数表.
- 评估ChatGPT-4,ChatGPT-3.5和Google Bard在创建炎症性肠病小鼠模型的分数表中的性能.
- 分析LLM生成的分数表,以确定结构一致性,准确性和干预适当性.
主要方法:
- 要求LLM根据预先定义的标准 (减肥,便一致性,便血液) 创建临床分数表.
- 评估了三个LLM (ChatGPT-4,ChatGPT-3.5,谷歌巴德) 的情况.
- 评估的关键参数包括输出结构,严重程度表示和干预门.
主要成果:
- 在分数表方面,LLM表现出一致的结构性输出.
- 在不同模型的数值症状评分和干预值定义中观察到显著的差异.
- 人工智能显示出起草的潜力,但需要人类专家的验证.
结论:
- 法律学士可以协助创建动物福利得分表的基础草案.
- 专家监督对于确保人工智能生成的临床前研究文档的准确性和可靠性至关重要.
- 将人工智能工具与专业判断相平衡,对于强有力的动物福利评估至关重要.
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