Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

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

Can trained behavior serve as tool for welfare assessment? A pilot study in a cancer model with Wistar rats.

Laboratory animals·2026
Same author

Urinary Dysfunction in Myasthenic Syndromes: A Scoping Review of Clinical Features and Treatment-Related Associations.

Muscle & nerve·2026
Same author

[The digital patient journey in radiological emergencies : Massive hemoptysis as a stress test of interoperability].

Radiologie (Heidelberg, Germany)·2026
Same author

[Digital patient journey after radiological diagnostics : Patient portals-what comes after the examination?]

Radiologie (Heidelberg, Germany)·2026
Same author

Correction: Analgosedation in interventional radiology.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin·2026
Same author

Externally Tested AI for Lung Nodule Classification: A Realistic Benchmark for an Emerging Screening Era.

Radiology. Artificial intelligence·2026

相关实验视频

Updated: Jun 20, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

简化干预放射学报告的大型语言模型:比较分析.

Elif Can1, Wibke Uller1, Katharina Vogt1

  • 1Department of Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany (E.C., W.U., K.V., M.C.D.).

Academic radiology
|October 1, 2024
PubMed
概括

在简化干预性放射学报告方面,GPT-4和Claude-3-Opus非常出色. 然而,所有大型语言模型 (LLM) 都显示出错误,需要进一步验证以供临床使用.

关键词:
人工智能的人工智能干预性放射学 干预性放射学大型语言模型耐心的友好耐心的友好结构化报告 结构化报告

更多相关视频

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

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

Published on: April 14, 2023

2.4K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

510

相关实验视频

Last Updated: Jun 20, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

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

Published on: April 14, 2023

2.4K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

510

科学领域:

  • 人工智能的人工智能
  • 医疗信息学 医疗信息学
  • 放射学 放射学是一门学科.

背景情况:

  • 简化干预放射学 (IR) 报告对于患者的理解和临床决策至关重要.
  • 大型语言模型 (LLM) 为自动化报告简化提供了潜力.

研究的目的:

  • 量化和质量地比较领先的专有和开源LLM在简化IR报告方面的表现.
  • 评估简化报告的准确性,清晰性,临床相关性和错误率.

主要方法:

  • 通过使用GPT-4,GPT-3.5 Turbo,Claude-3-Opus,Gemini Ultra,Mistral-7b和Mistral-8x7b,简化了109个IR报告.
  • 质量评估使用了五点的利克特度表,以确定准确性,完整性,清晰性,临床相关性,自然性和错误率.
  • 使用Flesch阅读易度,Flesch-Kincaid等级水平,SMOG指数和Dale-Chall可读性得分来测量定量可读性.

主要成果:

  • GPT-4和Claude-3-Opus表现出优越的质量性能,优于其他模型 (p < 0.001).
  • GPT-4显示最少的内容和破坏信任的错误,紧随其后的是Claude-3-Opus.
  • 在所有定量可读性指标 (p < 0.001) 中,GPT-4的表现也优于其他模型 (p < 0.001).

结论:

  • 在简化IR报告方面,GPT-4和Claude-3-Opus是最有效的LLM.
  • 尽管性能优异,但所有模型都出现了错误,包括破坏信任的错误,在临床部署之前需要进一步改进和验证.