评估GPT-4在放射图像分析中的多式联络性能
Dana Brin1,2, Vera Sorin3,4,5, Yiftach Barash3,4,5
1Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel. dannabrin@gmail.com.
European radiology
|August 30, 2024
概括
这项研究评估了GPT-4V用于解释放射图像,发现它准确地识别了成像模式,但与解剖区域和病理学有关. 目前的GPT-4V性能对于在放射学中临床使用是不可靠的.
科学领域:
- 医疗成像中的人工智能
- 放射学和诊断成像 放射学和诊断成像
- 计算机视觉在医疗保健中的应用
背景情况:
- 人工智能 (AI) 显示出增强放射学诊断过程的潜力.
- 像GPT-4V这样的多式人工智能模型可以分析图像和文本,为解释放射性扫描提供了新的可能性.
- 评估这些先进的人工智能模型的性能对于了解它们的临床实用性至关重要.
研究的目的:
- 评估多式人工智能模型GPT-4V在解释各种放射图像方面的性能.
- 将GPT-4V在识别成像模式,解剖区域和病理学方面的准确性与高级放射科医生进行比较.
- 探索零射击生成AI在放射诊断中的潜力.
主要方法:
- 使用GPT-4V分析了来自急诊室的230张匿名诊断图像.
- 图像包括超声波 (美国),计算机断层扫描 (CT) 和X射线模式.
- 为了准确性评估,GPT-4V的解释与高级放射科医生的解释进行了比较.
主要成果:
- 在识别成像模式方面,GPT-4V实现了100%的准确性.
- 解剖区域识别准确度因模式而异 (美国为60.9%,CT为97%,X射线为100%).
- 病理学识别准确度也差异很大 (美国9.1%,CT 36.4%,X射线 66.7%),诊断幻觉率很高.
结论:
- GPT-4V在放射学中显示出潜力,但由于性能不一致和高幻觉率,尚未可靠用于临床解释.
- 需要进一步开发,以提高GPT-4V的精度和可靠性,用于放射学诊断目的.
- 虽然很有希望,但GPT-4V目前不能在临床环境中作为独立工具使用.
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