使用大型语言模型来预测神经成像
Lleayem Nazario-Johnson1, Hossam A Zaki1, Glenn A Tung2
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
|July 9, 2023
概括
大型语言模型 (LLM) 在选择适当的神经辐射成像方面表现有希望,但经验丰富的神经辐射学家仍然比他们表现更好. 需要对LLM进行进一步的医学培训,以提高临床决策的准确性和一致性.
科学领域:
- 医疗人工智能 医疗人工智能
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 在医疗领域表现出越来越大的能力.
- 评估专业医疗任务的LLM对于了解其潜力和局限性至关重要.
研究的目的:
- 根据临床陈述,评估LLM在预测最佳神经辐射成像方式方面的能力.
- 为了比较LLMs的表现与经验丰富的神经放射科医生在这个任务中.
主要方法:
- 两个LLM (ChatGPT和Glass AI) 和一个神经放射学家评估了147种临床情景.
- 对答案的评分是根据ACR适当性标准进行的.
- 分析了LLM输出的一致性,并与专家表现进行了比较.
主要成果:
- LLM (ChatGPT: 1.75,Glass AI: 1.83) 的表现相似,但神经放射学家 (2.20) 的表现明显优于他们.
- 与玻璃AI相比,ChatGPT在其响应中表现出更大的不一致性.
- 在两个LLM的整体表现之间没有发现统计学上显著的差异.
结论:
- LLM能够建议适当的神经辐射成像,但需要进一步的医学培训以提高准确性.
- 目前的LLM在选择成像方式方面并没有超过专家神经放射学家.
- 持续开发是必要的,以提高在复杂的医疗应用中LLM的性能.
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