聊天GPT4-视觉可以在脑MRI上识别多发性硬化症的放射性进展吗?
Brendan S Kelly1,2,3,4, Sophie Duignan5, Prateek Mathur5
1St Vincent's University Hospital, Dublin, Ireland. brendanskelly@me.com.
European radiology experimental
|January 15, 2025
概括
GPT4视觉 (GPT4V) 模型在MRI扫描上显示了识别多发性硬化症 (MS) 进展的潜力,但尚未在临床上准备好. 像U-Net和Vision Transformer (ViT) 这样的专业模型目前在准确性方面优于GPT4V.
科学领域:
- 医疗成像中的人工智能
- 放射学研究 放射学研究
- 计算机视觉应用程序 计算机视觉应用
背景情况:
- 最新的GPT4-vision (GPT4V) 模型现在接受图像输入,为AI在医学诊断中开辟了新的可能性.
- 这项研究评估了GPT4V在MRI扫描上检测多发性硬化症 (MS) 进展的性能.
- 与已建立的深度学习模型,U-Net和Vision Transformer (ViT) 进行了比较.
研究的目的:
- 将GPT4V的诊断性能与U-Net和ViT进行比较,用于在MRI上识别MS进展.
- 评估GPT4V在零射击环境中的能力,以检测放射性变化.
- 评估GPT4V的准确性和可靠性,以确定MS的进展.
主要方法:
- 在2019-2021年期间,有170名多发性硬化症患者接受了MRI扫描.
- 配对,共同注册的MRI扫描 (有和没有进展) 在零射击实验中被用作GPT4V的输入.
- 使用准确度,精度,回忆和F1得分来评估性能,通过引导计算的95%置信区间.
主要成果:
- U-Net和ViT的准确率达到了94%,而GPT4V在识别多发性硬化症进展时的准确率达到了85%.
- 与U-Net和ViT相比,GPT4V的精度,回忆和F1得分都较低.
- 在某些情况下,GPT4V表现出谨慎的不响应,表明最终诊断的限制.
结论:
- 由于其可访问性和零射击能力,GPT4V显示了人工智能在放射学研究中的前景.
- 尽管有潜力,但由于错误分类和谨慎的反应,GPT4V还不适合临床使用.
- 需要进一步的研究和开发,以提高GPT4V在医疗应用中的准确性和可靠性.
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