概括
像ChatGPT和Bard这样的大型语言模型 (LLM) 在回答中间生命支持多选题时表现相似. 虽然有用,但AI模型都没有准确地回答所有问题,这表明需要进一步开发医学教育应用程序.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 临床实践 临床实践
背景情况:
- 大型语言模型 (LLM) 显示了通过解释多选题 (MCQ) 的答案来帮助医学教育的潜力.
- 在医学评估中评估LLM聊天机器人的有效性对于了解它们的有用性至关重要.
研究的目的:
- 评估ChatGPT和Bard在回答中等生命支持MCQ时的准确性和解释质量.
- 为了比较这些LLM聊天机器人在英国复苏委员会开发的测试中的性能.
主要方法:
- 聊天GPT-3.5和Bard在10个中等生命支持MCQ中进行了测试.
- 人工智能响应的准确性得分为 (40分和10分).
- 解释由三个医生使用0-3评分尺度来评估;Fleiss kappa评估了评分机构之间的可靠性.
主要成果:
- 巴德和ChatGPT在总体问题得分 (p=0.37) 和子问题得分 (p=0.26) 中表现相似.
- 两种LLM提供的解释质量是可比的.
- 两个聊天机器人都提供了有用的正确信息,即使在回答一些问题时也不正确.
结论:
- 聊天GPT和巴德在回答中级生命支持MCQ方面表现出类似的能力.
- 两种LLM都没有达到完美的准确性,突出了医疗教育中人工智能的持续发展需求.
- 需要进一步的研究来优化医疗培训和评估的LLM.
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