查特GPT在检测诊断错误及其贡献因素方面的性能评估:对545个诊断错误案例报告的分析
Yukinori Harada1, Tomoharu Suzuki2, Taku Harada3,4
1Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, Shimotsuga-gun, Tochigi, Japan yuki.gym23@gmail.com.
BMJ open quality
|June 3, 2024
概括
在95%的病例中,ChatGPT准确地识别出诊断错误. 这种人工智能工具在识别导致诊断错误的因素,特别是异常呈现方面也可能比人类审查更敏感.
科学领域:
- 医疗信息学医学信息学
- 医疗保健中的人工智能
- 临床诊断 临床诊断 临床诊断
背景情况:
- 手动查看图表是检测诊断错误的标准,但资源密集.
- 像ChatGPT这样的大型语言模型可以对文本进行分类,并协助检测错误.
- 本研究探讨了ChatGPT在识别诊断错误和贡献因素方面的实用性.
研究的目的:
- 评估ChatGPT在从案例展示中检测诊断错误方面的准确性.
- 评估ChatGPT识别导致诊断错误的因素的能力.
- 将ChatGPT的发现与医生评估进行比较.
主要方法:
- 分析了545份已发表的病例报告,其中存在诊断错误.
- 使用ChatGPT (GPT-4) 与病例介绍和诊断来识别错误和因素.
- 使用诊断错误评估和研究 (DEER),可靠诊断挑战 (RDC) 和通用诊断陷 (GDP) 分类学编码贡献因素.
- 将ChatGPT的因子识别与医生评估进行比较.
主要成果:
- 在95% (519/545) 的病例中,ChatGPT正确识别了诊断错误.
- 与医生相比,ChatGPT在DEER,RDC和GDP分类学中确定了每例显著更多的促成因素 (p<0.001).
- 通过ChatGPT确定的关键因素包括"考虑诊断的失败/延迟" (DEER) 和"异常呈现" (RDC和GDP).
结论:
- 聊天GPT在从临床病例介绍中检测诊断错误方面表现出很高的准确性.
- 与手动审查相比,ChatGPT显示了在识别导致诊断错误的因素,特别是"非典型呈现"方面提高敏感性的潜力.
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