概括
在5个大型语言模型 (LLM) 中,ChatGPT在实践放射检查中表现出最高的准确性. 由于可能存在错误,用户必须验证人工智能生成的答案的准确性.
科学领域:
- 医疗教育中的人工智能
- 放射学认证考试
- 自然语言处理
背景情况:
- 人工智能 (AI) 和大型语言模型 (LLM) 越来越多地融入各种专业领域,因此需要对其在专业领域的表现进行评估.
- 评估LLM在医学教育中的能力,特别是对于高风险的认证考试,对于了解其潜力和局限性至关重要.
研究的目的:
- 为了比较五个领先的大型语言模型 (LLMs) 在实践X光学认证考试中的表现.
- 确定哪个LLM表现出最高的准确性,并分析不同内容类别的性能.
主要方法:
- 一项探索性,非实验性研究使用了实践放射学认证考试中的200个选择题.
- 根据他们对这些问题的准确性评估了五个LLM:ChatGPT,Claude,Copilot,Gemini和Perplexity.
- 包括麦克纳马测试在内的统计分析用于确定准确率的显著差异,性能也按内容类别和子类别进行分类.
主要成果:
- 聊天GPT获得了最高的整体准确度,达到83.5%,明显超过了Claude和Gemini.
- ChatGPT在患者护理问题上表现出色,全部答案正确,在安全性和程序方面表现出色.
- 在图像制作方面,Copilot表现出最高的准确性,而ChatGPT则在八个子类别中的四个中领先.
结论:
- ChatGPT是评估实践放射学认证考试问题的最准确的大型语言模型.
- 尽管性能优异,但用户必须谨慎使用生成性AI,检查所有响应的准确性,因为有可能产生错误的信息.
- 未来的研究应该探索其他医学成像模式的认证考试的LLM表现,如MRI,核医学,放射治疗和超声波.
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