Jove
Visualize
联系我们

相关概念视频

Nursing Evaluation01:15

Nursing Evaluation

4.1K
The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
4.1K

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

Telemonitoring in Inflammatory Bowel Disease: Findings from the TIGE-Rus Randomized Controlled Trial.

Journal of clinical medicine·2026
Same author

RiTex: Harmonization of Radiomic Features Based on Riemannian Geometry.

Journal of imaging·2026
Same author

Comparison of artificial intelligence (AI) services for Breast Imaging-Reporting and Data System (BI-RADS) classification on mammograms.

Quantitative imaging in medicine and surgery·2026
Same author

Key aspects of fine-tuning and applying LLM-as-a-judge for clinical data summaries in the radiological workflow.

Frontiers in artificial intelligence·2026
Same author

Design and validation of a technology for 3D printing training phantoms for ultrasound imaging.

Physical and engineering sciences in medicine·2025
Same author

Pilot Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-Small-Cell Lung Cancer in the Moscow Population: A Step Toward Virtual Biopsy.

Journal of imaging·2025
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

相关实验视频

Updated: Jan 13, 2026

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

1.0K

使用自动评估指标和LLM-as-a-Judge方法评估医学文本摘要:一个试点研究

Yuriy Vasilev1, Irina Raznitsyna1, Anastasia Pamova1,2

  • 1Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, 127051 Moscow, Russia.

Diagnostics (Basel, Switzerland)
|January 10, 2026
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 显示出对电子健康记录 (EHR) 总结的希望. 然而,自动化质量控制方法,包括LLM-as-a-judge,难以检测事实上的错误,需要专家审查.

关键词:
作为法官的法学士电子健康记录是电子医疗记录.大型语言模型摘要 摘要 摘要 摘要 摘要

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

1.3K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.2K

相关实验视频

Last Updated: Jan 13, 2026

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

1.0K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

Published on: June 13, 2025

1.3K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

9.2K

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能

背景情况:

  • 电子健康记录 (EHR) 包含重要的临床数据,但难以处理.
  • 大型语言模型 (LLM) 为总结EHR数据提供了一个有希望的解决方案,以帮助医生.
  • 自动化质量控制对于将LLM总结工具整合到临床实践中至关重要.

研究的目的:

  • 评估LLM生成的医学摘要的自动化质量控制的可行性和局限性.
  • 在没有专家参与的情况下评估自动指标和LLM-as-a-judge方法.

主要方法:

  • 六个开源的LLM从30个EHR文本样本中生成了摘要.
  • 总结使用标准指标 (BLEU,ROUGE,METEOR,BERTScore) 和LLM-as-a-judge进行了评估.
  • 标准包括相关性,完整性,冗余性,连贯性,语法,术语和幻觉检测.
  • 专家评估使用相同的标准进行了比较.

主要成果:

  • 在医学数据总结方面,LLM具有显著的潜力.
  • 无论是自动指标还是LLM法官都无法可靠地检测事实错误或语义扭曲 (幻觉).
  • 在LLM总结质量评分和有关相关性的专家意见之间观察到0.688的皮尔森相关性.

结论:

  • 完全自动化医疗摘要的质量评估仍然是一个重大挑战.
  • 未来的研究应该优先考虑幻觉检测方法,并探索更大,更专业的医学文本LLM.
  • 将检索增强生成 (RAG) 集成到LLM-as-a-judge架构中需要进一步调查.