评估大型语言模型对角相关问题的答案的可靠性
Mustafa Kayabaşı1, Seher Köksaldı1, Ceren Durmaz Engin2
1Department of Ophthalmology, Mus State Hospital, Mus, Turkey.
Clinical & experimental optometry
|October 24, 2024
概括
大型语言模型 (LLM) 在医疗保健方面表现有前途. 与Copilot和Gemini相比,ChatGPT-4在回答角 (KCN) 问题方面表现优异,尽管可读性较低.
科学领域:
- 人工智能在医疗保健中的应用.
- 大型语言模型 (LLM) 的进展.
背景情况:
- 大型语言模型 (LLM) 为医疗保健查询提供类似人类的响应.
- 有限的研究存在于LLM对角 (KCN) 信息的能力.
研究的目的:
- 评估三个LLM (ChatGPT-4,Copilot,Gemini) 在回答患者关于角质 (KCN) 问题的表现.
- 评估有关KCN的LLM生成响应的准确性,可靠性和可读性.
主要方法:
- 在KCN上向ChatGPT-4,Copilot和Gemini提出了50个常见的患者问题.
- 眼科医生使用5点的利克特等级来评价反应.
- 使用DISCERN和EQIP尺度评估了LLM的可靠性;使用Flesch和Coleman-Liau指数测量了可读性.
主要成果:
- 与Copilot (34%) 和Gemini (42%) 相比,ChatGPT-4获得了显著更高的"强烈同意"评分 (74%) (p < 0.001).
- 在一般信息和症状/诊断类别中,ChatGPT-4表现优越.
- 尽管可读性较低,复杂性更高,但ChatGPT-4被认为是KCN查询中最可靠的.
结论:
- 在评估的模型中,ChatGPT-4是最熟练的LLM来回答与角质 (KCN) 相关的问题.
- 虽然LLM响应通常需要提高可读性,但ChatGPT-4为患者提供了最可靠的信息.
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