聊天GPT可以通过MRCP (英国) 书面考试吗? 使用临床决策推理框架分析性能和错误
Amy Maitland1, Ross Fowkes1, Stuart Maitland2,3
1Health Education England North East, Newcastle upon Tyne, UK.
BMJ open
|March 15, 2024
概括
像ChatGPT这样的大型语言模型 (LLM) 在研究生医学考试中表现强,超过了通过分数. 分析他们的错误,如事实和遗漏错误,是改善医疗保健人工智能的关键.
科学领域:
- 人工智能在医学中的应用
- 医疗教育 技术 技术 医学教育
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地被纳入医疗保健,用于研究,教育和临床支持.
- 对LLM的监管审查正在随着其在医疗环境中的采用而增长.
- 在标准化评估中评估LLM绩效对于了解其能力和局限性至关重要.
研究的目的:
- 分析聊天GPT 4关于皇家医师学院 (MRCP) 成员资格的部分1和2书面考试的表现.
- 确定ChatGPT在医学研究生问题上的表现的具体优缺点.
- 提供关于LLM在医疗保健应用中的潜在作用和局限性的见解.
主要方法:
- 聊天GPT 4 (2023年5月24日版本) 使用MRCP部分1和2的实践问题进行了测试.
- 统计分析,包括斯皮尔曼等级相关性,检查了准确性,问题难度和问题长度之间的关系.
- 错误的答案使用临床推理框架进行了定性分析,以分类错误.
主要成果:
- 聊天GPT实现了高准确率:MRCP第1部分为86.3%,MRCP第2部分为70.3%.
- 在ChatGPT的准确性和通过率 (第二部分) 和问题长度 (第一部分) 之间观察到显著的相关性.
- 发现的最常见的错误是事实错误,上下文错误和省略错误.
结论:
- 聊天GPT的表现超过了两个MRCP考试的通过门,证明了它作为AI知识评估的基准的潜力.
- 多选题考试是评估LLM绩效与人类专业知识的宝贵工具.
- 了解LLM错误的性质对于开发更安全,更有效的AI驱动医疗设备至关重要.
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