一个大型语言模型在日本紧急医疗委员会认证考试中的表现
Yutaka Igarashi1, Kyoichi Nakahara1, Tatsuya Norii2
1Department of Emergency and Critical Care Medicine, Nippon Medical School.
概括
大型语言模型 (LLM) 在急诊医学考试中表现令人满意,正确回答了62.3%的问题. 然而,事实上的错误需要医生监督AI在重症监护.
科学领域:
- 医学教育 医学教育
- 人工智能在医学中的应用
- 紧急医疗 紧急医疗
背景情况:
- 紧急医生需要对关键条件有广泛的知识.
- 人工智能 (AI) 和大型语言模型 (LLM) 在医疗保健方面表现有前途.
- 在紧急医疗中LLM的有效性尚未确定.
研究的目的:
- 为了评估ChatGPT的可靠性,一个大型语言模型 (LLM).
- 评估在紧急医疗委员会认证考试上的LLM绩效.
主要方法:
- 从2018年到2022年,ChatGPT管理了日本急性医学协会董事会认证问题.
- 该LLM回答每一个问题两次,以评估答案的一致性.
- 使用统计分析,包括kappa统计,来评估一致性和准确性.
主要成果:
- 该LLM的整体正确答案率为62.3% (465/475个问题).
- 基于文本的问题 (65.9%) 的表现高于基于图像的问题 (52.0%).
- 事实错误占不正确答案的82%,在LLM的两个答案之间存在实质上的一致性 (kappa = 0.70).
结论:
- 在紧急医疗检查中,LLM的表现令人满意,特别是在只有文本问题的检查中.
- 事实错误的普遍性强调了在临床实践中使用LLM时需要医生监督的必要性.
- 需要进一步的研究来完善LLM的准确性和整合到紧急医疗工作流程中.
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