大型语言模型在诊断和管理具有挑战性的临床病例中的比较
Sujeeth Krishna Shanmugam1, David J Browning1
1Department of Ophthalmology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Clinical ophthalmology (Auckland, N.Z.)
|November 18, 2024
概括
大型语言模型 (LLM) 在帮助眼科医生处理复杂病例方面表现有前途,尽管准确性还不足以独立照顾患者. 在这个眼科病例分析中,基于订阅的LLM通常表现更好.
科学领域:
- 人工智能在医学中的应用
- 眼科医生 眼科 眼科
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医学应用.
- 评估LLM在眼科等专业领域的表现对于了解其潜力和局限性至关重要.
研究的目的:
- 将各种大型语言模型 (LLM) 的分析和响应能力与具有挑战性的眼科案例研究进行比较.
- 评估眼科LLM的准确性,差异诊断,诊断测试和治疗建议表现.
主要方法:
- 十五个可访问的大型语言模型 (LLM) 在JAMA眼科的20个困难的眼科病例研究中进行了测试.
- 案例以开放文本形式呈现,要求差异诊断,诊断测试和治疗建议.
- 对LLM答案的准确性与已确定的正确答案进行了评估,并对子测试性能和答案可读性进行了二次分析.
主要成果:
- 三个LLM (ChatGPT 3.5,Claude Pro,Copilot Pro) 在统计学上显示出高于平均水平的显著表现.
- 付费订阅模式 (Claude Pro,Copilot Pro) 是表现最好的一种,Copilot Pro显示出轻微的优势.
- 所有测试的LLM都产生了超过公众消费者推的八年级水平的可读性,尽管专业理解得到了确认.
结论:
- 排名很高的LLM,特别是基于订阅的LLM,显示出眼科助理的潜力.
- 目前的LLM准确性对于独立的患者护理是不够的,但在帮助临床医生和防止忽视方面显示出有前途.
- 虽然LLM答案的可读性超过了公众推,但对于经过董事会认证的眼科医生来说是可以理解的.
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