评估一个大型语言模型对急性胸部疾病的胸部X射线解释的准确性
1Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA, USA.
The American journal of emergency medicine
|April 2, 2025
概括
像ChatGPT这样的大型语言模型在解释紧急情况的胸部X射线方面表现有前途,准确识别正常扫描,但对于特定的病理需要改进. 进一步的研究可以提高其在放射学中的诊断能力.
科学领域:
- 医疗保健中的人工智能
- 医学成像分析 医学成像分析
- 紧急辐射学 紧急辐射学
背景情况:
- 人工智能 (AI) 正在快速发展,具有改变医疗保健提供的巨大潜力.
- 胸部X射线对于在急诊室 (ED) 诊断急性胸部疾病至关重要.
- 胸部X射线的解释延迟可能会阻碍及时的临床决策;人工智能模型提供潜在的诊断支持,但紧急放射学中的大语言模型 (LLMs) 尚未得到充分探索.
研究的目的:
- 评估ChatGPT在解释胸部X射线用于ED中常见的急性胸部疾病的可行性.
- 为了评估ChatGPT 4.0在七个不同的病理类别中的诊断性能,使用"X射线解释器"附加组件.
主要方法:
- 来自NIH胸部X射线数据集的1400张胸部X射线图像的分析.
- 将图像分为七种病理的分类:心电,输液,肺,肺胸,肺炎,质量和没有发现.
- 使用"X射线解释器"附加组件评估ChatGPT 4.0的诊断性能.
主要成果:
- 在识别正常的胸部X射线 (灵敏度98.9%,特异性93.9%,准确性94.7%) 中,ChatGPT实现了高性能.
- 诊断性能在病理学上有所不同;最好的结果是肺炎 (灵敏度76.2%,特异性93.7%) 和肺胸 (灵敏度77.4%,特异性89.1%).
- 在检测乳腺脱落和乳腺突发症方面,表现较低.
结论:
- 聊天GPT显示了作为一个辅助工具的潜力,用于区分正常与异常的胸部X射线,特别是在肺炎等疾病.
- 对于微妙的疾病的诊断准确性需要提高.
- 未来的研究应该探索将ChatGPT与专门的图像识别模型集成,以提高医学成像和教育方面的性能.
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