使用ChatGPT-4对微软Bing进行评估,用于评估腹部计算机断层扫描和磁共振图像
Alperen Elek1, Duygu Doğa Ekizalioğlu2, Ezgi Güler2
1Ege University Faculty of Medicine, İzmir, Türkiye.
Diagnostic and interventional radiology (Ankara, Turkey)
|August 19, 2024
概括
使用ChatGPT-4的微软Bing可以识别腹部CT和MRI扫描,但难以准确检测异常. 为了在放射学中可靠的临床使用,需要进一步开发.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医学诊断中的潜力.
- 在放射学中评估像微软Bing这样的AI工具与ChatGPT-4对于理解它们的临床实用性至关重要.
- 腹部成像,包括计算机断层扫描 (CT) 和磁共振成像 (MRI),存在复杂的诊断挑战.
研究的目的:
- 评估由ChatGPT-4技术支持的微软Bing在分析腹部CT和MRI扫描中的性能.
- 将AI的解释与放射科医生对关键成像特征和异常检测的评估进行比较.
主要方法:
- 进行了80个腹部CT和MRI扫描的比较分析.
- 微软Bing的解释被评估为模式识别,解剖识别,成像平面,MRI序列,对比度使用和异常检测.
- 结果与专家放射科医生的印象进行了比较.
主要成果:
- 在识别成像模式 (CT:95.4%,MRI:86.1%) 和腹部区域 (83.75%),Bing实现了高精度.
- 在识别成像平面 (CT: 95.4%,MRI: 83.3%) 和MRI序列 (68.75%),性能各不相同.
- Bing仅在35%的病例中检测到异常,正确诊断率为10.7%,表明临床解释的局限性.
结论:
- 使用ChatGPT-4的微软Bing显示了对腹部CT和MRI的基本图像识别任务的能力.
- 人工智能目前无法可靠地解释异常,这限制了它在放射学中的直接临床应用.
- 对LLM的持续改进是必要的,以加强其诊断支持和在医学成像中的临床整合.
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