革命还是风险? - - 评估GPT-4V在放射图像解释方面的潜力和挑战
Marc Sebastian Huppertz1, Robert Siepmann2, David Topp2
1Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. mhuppertz@ukaachen.de.
生成型预训练型变压器4视觉 (GPT-4V) 在解释放射图像方面显示出有限的诊断准确性,特别是在没有临床背景的情况下. 人工智能模型捏造发现并错误识别细节,对患者安全构成风险.
科学领域:
- 医疗成像中的人工智能
- 多模式大型语言模型 (LLM)
- 放射学 诊断 诊断 放射学
背景情况:
- 生成式预训练变压器4视觉 (GPT-4V) 是一个先进的多式联络LLM,能够处理基于图像的查询.
- 人工智能工具在临床实践中的整合需要对其诊断能力进行严格的评估.
研究的目的:
- 在自主解释各种临床成像研究时评估GPT-4V的诊断性能.
- 为了比较GPT-4V的准确性,并没有提供临床和人口统计信息.
主要方法:
- 评估了206项具有确定的诊断的临床成像研究 (放射学,CT,MRI,血管学).
- 进行了GPT-4V读数的非上下文化 (仅图像) 和上下文化 (图像附加信息).
- 用多个维度的统计分析来评估诊断准确性.
主要成果:
- 诊断准确度显著提高,从8.3% (非上下文化) 提高到29.1% (上下文化,首次诊断) 和63.6% (上下文化,差异性) (p ≤ 0.001).
- 该模型表现出倾向于偏好上下文而不是图像数据,在重新评估时准确性下降.
- 在65张图像中,GPT-4V在412个响应中伪造了成像发现,并在65张图像中错误识别了模式/区域.
结论:
- 在目前的代中,GPT-4V对于自主解读放射图像是不可靠的.
- 模型倾向于忽视图像细节,捏造发现和误解信息,这对患者安全构成风险.
- 进一步完善多式联络LLM对于其在放射学中的安全有效应用至关重要.
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