多模式大语言模型的能力解释儿科放射图像
Thomas P Reith1, Donna M D'Alessandro2, Michael P D'Alessandro3
1Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA. thomaspreith@gmail.com.
Pediatric radiology
|August 12, 2024
概括
多模式大语言模型 (LLM) 在解释儿科放射图像时显示有限的准确性. 这些人工智能工具还没有准备好在这个专业领域临床使用.
科学领域:
- 医疗成像中的人工智能
- 儿科放射学研究儿童放射学研究
- 医疗保健中的机器学习
背景情况:
- 有限的人工智能 (AI) 开发存在于儿科放射学.
- 新的多式大型语言模型 (LLM) 可以处理文本,图像和视频输入.
- 从理论上讲,LLM可以解释放射图像,包括儿科病例.
研究的目的:
- 评估儿科放射图像上的多模式LLM的诊断能力.
- 在专业医疗领域评估先进AI模型的准确性.
主要方法:
- 使用了30个显著的儿科放射病例,共计90张图像.
- 三个领先的多式联络LLM (GPT-4,Gemini 1.5 Pro,Claude 3 Opus) 进行了测试.
- 人工智能解释是由居民和主治医生独立验证的.
主要成果:
- 人工智能模型仅在27.8%的图像中实现了正确的诊断.
- 在13.3%的病例中观察到部分正确性.
- 大多数解释 (58.9%) 是不正确的.
结论:
- 目前的多模式LLM对于解释儿科放射图像的准确性不足.
- 在儿科放射学中临床应用之前,需要进一步开发AI.
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