大型语言模型对诊断推理的影响:一项随机临床贴片研究
Ethan Goh1,2, Robert Gallo3, Jason Hom4
1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.
medRxiv : the preprint server for health sciences
|April 1, 2024
概括
与传统方法相比,使用GPT-4作为诊断辅助剂并没有显著改善医生的诊断推理. 然而,单独GPT-4的表现优于医生,这表明AI辅助临床决策的潜力.
科学领域:
- 医疗人工智能的人工智能
- 临床决策支持系统临床决策支持系统
- 医生的诊断推理 医生的诊断推理
背景情况:
- 诊断错误是导致患者发病的一个重要原因.
- 大型语言模型 (LLM) 在医学推理评估中表现有前途.
- 在实际的临床诊断推理LLMs的影响仍然不清楚.
研究的目的:
- 评估GPT-4大语言模型对医生诊断推理能力的影响.
- 在临床环境中,将GPT-4的性能与传统诊断资源进行比较.
主要方法:
- 一个多中心,随机的临床图片研究,涉及50名医生 (住院医生和临床医生).
- 参与者被分配使用GPT-4与常规资源或仅使用常规资源.
- 医生有60分钟的时间来分析六个临床细节,以诊断性能和每病例的时间为主要结果.
主要成果:
- 在GPT-4组和常规资源组之间没有观察到诊断推理得分的显著差异 (中位差1.6个百分点,p=0.60).
- GPT-4组显示了更快完成案件的趋势 (中位数差异为-82秒,p=0.20).
- 当单独使用GPT-4时,其得分明显高于传统资源组 (15.5个百分点高,p=0.03).
结论:
- 在这项研究中,GPT-4作为诊断辅助剂并没有显著提高医生的诊断推理.
- 单独的GPT-4显示出卓越的性能,这表明AI在临床实践中的潜在好处.
- 需要进一步的研究来优化医生-AI合作,以提高诊断准确性和效率.
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