相关实验视频

Updated: Jul 27, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

大型语言模型的准确性作为光学教育的新工具

Genis Cardona¹, Marc Argiles¹, Lluis Pérez-Mañá¹

¹Department of Optics and Optometry, Universitat Politècnica de Catalunya, Terrassa, Spain.

Clinical & experimental optometry

|December 3, 2023

概括

像ChatGPT这样的人工智能 (AI) 工具在光学教育中显示出潜力,但需要经过专家的仔细审查. 他们的答案,特别是参考文献,需要仔细审查,以确保准确性和在学术环境中负责任地使用.

科学领域:

视光计学教育教育视光计学教育
医疗保健中的人工智能
眼科眼科研究眼科研究

背景情况:

像ChatGPT这样的大型语言模型 (LLM) 越来越多地被学生和研究人员采用.
法律学士的自信语气可以掩盖科学和临床查询答案的局限性.
在光学教育中无监督的AI整合可能会阻碍临床知识和技能的获取.

研究的目的:

评估视光学中ChatGPT响应的准确性和参考质量.
评估学生和专家对ChatGPT在学术光学中的实用性的看法.
调查查询特异性对AI响应质量的影响.

主要方法:

聊天GPT被询问了隐形眼镜,低视力和双眼视力主题.
专家和学生对答案的准确性进行了评估 (0-10级).
通过ChatGPT提供的引用被评估为准确性和相关性.

主要成果:

平均准确度分数在6-8 (专家) 和7.5-9 (学生) 之间.
更具体的查询在两组都获得了较低的准确度分数 (p<0.001).
只有24%的引用是准确的,而19.3%是相关的.

结论:

专家对ChatGPT响应和引用的评估对于学术和研究使用至关重要.
需要采取积极的措施来解决LLM的局限性,因为它们的使用正在扩大.
人工智能工具的负责任整合对于光学教育至关重要.

关键词:

人工智能的人工智能是人工智能.大型语言模型伦理学伦理伦理学视光学教育的教育.

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

相关概念视频

Improving Translational Accuracy

Improving 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...

Improving Translational Accuracy

Improving 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...