大型语言模型在儿童放射学中检测需要立即报告的病例的准确性:使用公开可用的临床图片的可行性研究
Jun Sung Park1, Jisun Hwang2, Pyeong Hwa Kim3
1Department of Pediatrics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Korean journal of radiology
|August 28, 2025
概括
多模式大语言模型 (LLM) 在识别急需的儿科放射病例方面具有与人类读者相美的准确性. 然而,它们在检测关键发现方面的可靠性需要改善,以便在临床上使用.
科学领域:
- 医学的人工智能
- 儿童放射学
- 医学成像分析
背景情况:
- 在儿科放射学中准确及时检测关键发现对于患者的结果至关重要.
- 目前的工作清单优先级依赖于人类解释,这可能耗时且容易延误.
研究的目的:
- 评估可视化多式大型语言模型 (LLM) 的诊断准确性,以确定需要立即报告的儿科放射病例.
- 将LLM的表现与人类读者的表现进行比较,以确定工作列表的优先级.
主要方法:
- 七个LLM (在两个温度设置下) 和四个人类阅读器对71个儿童临床图片进行了评估.
- 病例根据韩国分辨和敏度度量 (KTAS) 1-2级或临界值报告 (CVR) 标准进行分类.
主要成果:
- 在检测立即报告病例方面,LLM的准确率达到60.6%-83.1%. 双子闪光 (t1) 显示了最高的LLM精度 (83.1%),与最好的人类阅读器 (84.5%) 相美.
- 对关键价值报告的LLM敏感度很低 (Gemini-Flash t1的27.3%).
结论:
- 多模式LLM显示了与人类读取器相似或超过的儿童放射工作列表优先级的潜力.
- 在临床广泛采用之前,需要进一步开发以提高LLM对关键发现的敏感性.
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