在胸部CT诊断中评估ChatGPT-4V:一个批判性的图像解释评估
Reza Dehdab1, Andreas Brendlin2, Sebastian Werner2
1Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076, Tuebingen, Germany. reza.dehdab@med.uni-tuebingen.de.
Japanese journal of radiology
|June 12, 2024
概括
聊天GPT-4V在解释胸部CT扫描中的COVID-19和非小细胞肺癌 (NSCLC) 的诊断准确性有限. 虽然性能各不相同,但人工智能工具需要显著改进,以便在放射性诊断中可靠使用.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 人工智能的进步为增强放射性诊断提供了潜力.
- 跨模式的人工智能模型在解释复杂的医疗图像 (如CT扫描) 中提出了独特的挑战.
研究的目的:
- 评估ChatGPT-4V的诊断准确性,用于解释COVID-19的胸部CT切片,非小细胞肺癌 (NSCLC) 和对照病例.
- 评估ChatGPT-4V作为放射性诊断中的AI工具的潜力.
主要方法:
- 使用ChatGPT-4V.对60个胸部CT扫描 (COVID-19,NSCLC,对照) 的回顾性分析.
- 每次扫描中选择四个代表性的CT切片用于AI解释.
- 人工智能诊断与黄金标准诊断的比较,由两名放射科医生验证.
主要成果:
- 聊天GPT-4V的整体诊断准确率为56.76%.
- 敏感性和特异性在不同疾病之间有所不同:NSCLC (27.27%的敏感性,60.47%的特异性),COVID-19 (13.64%的敏感性,64.29%的特异性),对照组 (31.82%的敏感性,95.24%的特异性).
- 当所有肺叶都受到影响时,观察到最高的灵敏度 (83.33%);根据病理位置和叶片参与,发现了显著的差异.
结论:
- 在胸部CT解释中,ChatGPT-4V显示了可变的诊断性能.
- 放射学中跨模式人工智能模型存在重大挑战,突出了需要改进的领域.
- 增强人工智能模型对于它们在医学诊断中的可靠性和更广泛应用至关重要.
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