学习用生成性人工智能协助进行罕见和复杂的诊断:对流行的大型语言模型的定性研究
Tassallah Abdullahi1, Ritambhara Singh1,2, Carsten Eickhoff3
1Department of Computer Science, Brown University, Providence, RI, United States.
JMIR medical education
|February 13, 2024
概括
像Bard和GPT-4这样的大型语言模型 (LLM) 在帮助罕见疾病诊断方面表现有前途,优于人类共识和专业模型. 快速工程是关键,但没有一个策略适合所有LLM.
科学领域:
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
- 临床决策支持 临床决策支持
背景情况:
- 由于专家知识有限,罕见和复杂的疾病带来了诊断挑战.
- 大型语言模型 (LLM) 为临床决策支持和医学教育中的知识聚合提供了潜力.
研究的目的:
- 探索Bard,ChatGPT-3.5和GPT-4对罕见和复杂疾病的诊断能力.
- 调查快速工程对医学诊断的LLM绩效的影响.
主要方法:
- 对公开可用的罕见和复杂疾病病例进行的实验.
- 评估各种快速策略 (开放式,多选项) 和多数投票策略.
- 与人类受访者和MedAlpaca相比,LLM表现的比较.
主要成果:
- 所有测试的LLM都在诊断罕见和复杂病例方面超过了人类的平均共识和MedAlpaca.
- 使用多数投票策略的GPT-4获得了最高的整体得分.
- 巴德和GPT-4在MIMIC-III数据上的多选提示显示了高的诊断准确性 (93%).
结论:
- 在罕见和复杂疾病方面,LLM具有显著的诊断能力.
- 快速工程至关重要,但需要针对不同LLM量身定制的策略.
- 这项研究支持开发基于LLM的医疗教育和诊断辅助工具.
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