GPT-5.0可以解释甲状腺超声波图像吗? 一个比较的TI-RADS分析与专家放射科医生
Yunus Yasar1, Sevde Nur Emir1, Muhammet Rasit Er1
1Department of Radiology, Umraniye Training and Research Hospital, University of Health Sciences, 34668 Istanbul, Turkey.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
像GPT-5.0这样的大型语言模型显示出使用TI-RADS标准解释甲状腺超声波的潜力,但与特定特征作斗争,并倾向于高估恶性瘤风险,限制目前的临床使用.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 放射学 放射学是一门学科.
背景情况:
- 大型语言模型 (LLMs) 正在探索医学图像解释,包括甲状腺超声波 (USs).
- 评估甲状腺结节需要评估微妙的回声和形态细节,这对AI来说是一个挑战.
- 美国放射学学院 (ACR) 甲状腺成像报告和数据系统 (TI-RADS) 为评估甲状腺结节提供了一个标准化的框架.
研究的目的:
- 根据TI-RADS标准,评估GPT-5.0在解释美国甲状腺图像中的性能.
- 将GPT-5.0的评估与专家放射科医生的评估进行比较.
- 为了确定甲状腺结节恶性瘤风险分层的GPT-5.0的诊断准确性.
主要方法:
- 美国100例甲状腺病例的回顾性分析,细胞学证实了诊断 (贝塞斯达II,V-VI).
- 由GPT-5.0和放射科医生对TI-RADS特征 (组成,回声性,形状,边缘,回声焦点) 的独立评估.
- 使用科恩的卡帕和诊断性表现指标 (灵敏度,特异性) 分析与TR4-TR5作为恶性瘤阳性之间的评估者协议的分析.
主要成果:
- GPT-5.0在组成,形状和边缘方面与放射科医生达成了实质性一致,但在回声性方面只有中等程度,而在回声焦点方面则很差.
- GPT-5.0表现出高分类结节的倾向,分配更多的TR4-TR5类别,表明风险不良偏差.
- 与GPT-5.0 (灵敏度67%,特异性49%) 相比,放射科医生取得了更高的诊断性能 (灵敏度89%,特异性85%),GPT-5.0经常错误地将良性结节归类为可疑的.
结论:
- GPT-5.0可以识别一些TI-RADS特征,但与微化作斗争,并高估恶性瘤风险.
- 目前的性能限制了GPT-5.0在甲状腺结节风险分层方面的独立临床实用性.
- 特定领域的培训和适应可能会增强LLM在甲状腺结节评估中的辅助作用.
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