大型语言模型在放射学决策中的应用
Hossam A Zaki1, Andrew Aoun2, Saminah Munshi1
1Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University/Rhode Island Hospital, Providence, Rhode Island.
Journal of the American College of Radiology : JACR
|January 15, 2024
概括
玻璃AI和ChatGPT,大型语言模型 (LLM),显示出预测适当的医学成像研究的潜力. 玻璃AI表现出卓越的性能,这表明专业医疗培训可以增强放射学LLM的能力.
科学领域:
- 人工智能在医学中的应用
- 放射学决策支持 放射学决定支持
背景情况:
- 大型语言模型 (LLM) 正在迅速发展.
- 它们在医疗领域的应用,特别是放射学,还没有得到充分的研究.
研究的目的:
- 评估LLMs在预测各种临床场景最佳成像研究方面的能力.
- 为了将LLM的绩效与已建立的放射学指南进行比较.
主要方法:
- 两个LLM,ChatGPT和Glass AI,在11个美国放射学学院 (ACR) 专家小组的1,075个临床病例上进行了测试.
- 性能与ACR适当性标准相比,使用0-3分级来衡量,每个小组的结果均为平均值.
主要成果:
- 玻璃AI (2.32 ± 0.67) 的表现明显超过了ChatGPT (2.08 ± 0.74).
- 这两种模型在多创伤,乳腺和血管成像方面表现最好,在神经,肌肉骨和心脏面板方面表现最差.
- 在11个子专业中,玻璃人工智能在10个方面表现出色.
结论:
- LLM 显示出有助于放射学决策的前景.
- 专业医疗培训,如在玻璃AI中所见,似乎可以提高LLM在预测成像研究中的表现.
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