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使用ChatGPT-4.0,谷歌Gemini和微软Copilot对有关角质问题的答案的评估:对角质的大型语言模型进行比较研究
1Department of Ophthalmology, Adana 5 Ocak State Hospital, Adana, Turkey .
Eye & contact lens
|December 4, 2024
概括
与谷歌Gemini和微软Copilot相比,ChatGPT-4.0在回答患者关于角质的问题上表现出更高的准确性. 虽然更复杂,但它的反应被认为更可靠的临床信息传播.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 对于向患者和医生传播准确的临床信息越来越重要.
- 评估LLM在专业医疗领域的有效性对于安全有效的实施至关重要.
研究的目的:
- 评估ChatGPT-4.0,谷歌Gemini和微软Copilot在响应患者关于角膜的询问方面的有效性.
- 为了比较LLM生成的对常见的角质问题的答案的准确性,可靠性和可读性.
主要方法:
- 两位眼科医生盲目评价了LLM对25个常见的角问题在5点的利克特级别上的答案.
- DISCERN尺度评估了响应可靠性,而Flesch指数则测量了可读性.
主要成果:
- 聊天GPT-4.0提供了比Gemini和Copilot更详细和准确的答案 (92%"同意"或"强烈同意") .
- 在利克特尺度上,在所有三个LLM之间都发现了统计学上显著的差异 (P <0.001).
结论:
- 聊天GPT-4.0提供了更可靠和准确的关于角的信息,尽管患者理解的复杂性可能更高.
- 在临床信息传递方面,LLM表现有前途,但复杂性需要考虑患者的理解.
相关概念视频
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...