Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

A Web3-Based Patient-Centric Health Data Management System.

Studies in health technology and informatics·2026

Same author

Causal Inference on Air Pollutants and Atopic Dermatitis Outpatient Visits in Young Children: An Instrumental Variable Approach.

Studies in health technology and informatics·2026

Same author

Causal Effect of Air Pollution on Outpatient Visits for Chronic Rhinitis.

The Laryngoscope·2026

Same author

Risk of congenital malformation in newborns from mothers with kidney diseases in a nationwide cohort study.

Communications medicine·2026

Same author

An interactive cascaded deep learning framework with expert refinement for accurate striatal subregion segmentation.

Scientific reports·2026

Same author

Beyond Fine-Tuning: Leveraging Domain-Aware In-Context learning with large language models for clinical named entity recognition.

Journal of biomedical informatics·2026

Search research articles

相关实验视频

Updated: May 23, 2025

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

大型语言模型辅助系统审查:基于柯克伦审查数据的验证

Siun Kim¹, Hyung-Jin Yoon^2,3

¹Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea.

Studies in health technology and informatics

|May 17, 2025

概括

大型语言模型 (LLM) 显示出对系统审查的自动化有希望,GPT-4o在抽象选方面表现出色. 然而,偏见风险评估的准确性在不同领域有所不同,这表明目前的局限性.

关键词:

抽象的选抽象的选大型语言模型偏见的风险偏见的风险系统审查是系统的审查.

更多相关视频

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

相关实验视频

Last Updated: May 23, 2025

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

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
基于证据的医学基于证据的医学.

背景情况:

系统性审查对于以证据为基础的医学至关重要,但耗时.
大型语言模型 (LLM) 提供了一个机遇,可以自动化这一过程的部分.

研究的目的:

评估高级LLM (GPT-4o,GPT-4o-mini,Llama 3.1:8B) 在自动化系统审查任务方面的表现.
评估LLM在抽象选和偏差评估风险中的实用性.

主要方法:

在抽象查和偏差风险评估方面,LLM被测试,使用12个Cochrane药物干预审查.
为值调节提出了一种新的一次性包容性调整方法.

主要成果:

GPT-4o显示了最高的选性能 (回忆率为0.894,精度为0.492).
偏差风险评估的准确性取决于域,在随机序列生成中准确度最高 (0.873),在选择性报告中最低 (0.418).

结论:

在系统审查的自动化中,LLM显示出实际的实用性,特别是在抽象选中.
目前在系统审查中的LLM应用具有局限性,特别是在对偏见评估的细微风险方面.