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

Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

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

Radiology report quality improvement through structured provider feedback: a data-driven initiative.

Abdominal radiology (New York)·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Exceptional Longevity Modifying Allele APOE2 Promotes DNA Signaling Pathways Resisting Cellular Senescence in Human Neurons.

Aging cell·2026
Same author

Advances in Protein Function Prediction from the Fifth CAFA Challenge.

bioRxiv : the preprint server for biology·2026
Same author

Using Artificial Intelligence to Improve Timeliness of Follow-Up in Breast Cancer Screening.

Journal of the American College of Radiology : JACR·2026
Same author

Persistent Delays in Diagnostic Evaluation Timeliness After an Abnormal Screening Mammogram in the Years following the Onset of the COVID-19 Pandemic.

Academic radiology·2026
Same journal

ACR Appropriateness Criteria® Myelopathy: 2026 Update.

Journal of the American College of Radiology : JACR·2026
Same journal

ACR Appropriateness Criteria® Chronic Knee Pain: Update 2026.

Journal of the American College of Radiology : JACR·2026
Same journal

Reply.

Journal of the American College of Radiology : JACR·2026
Same journal

Radiation Sensibilities: The American College of Radiology Dose Index Registry Empowers Stakeholders in Radiation Dose Optimization.

Journal of the American College of Radiology : JACR·2026
Same journal

Supply Chain Vulnerabilities in Breast Imaging: Site- and Network-Level Strategies for a Concentrated Consumable Market.

Journal of the American College of Radiology : JACR·2026
Same journal

Prostate MRI Practices and PI-RADS Use in China's Mainland: A Nationwide Assessment and Opportunities for Standardization.

Journal of the American College of Radiology : JACR·2026
查看所有相关文章

相关实验视频

Updated: Jun 19, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K

在临床实施之前建立基于成像的人工智能算法的验证基础设施.

Ojas A Ramwala1, Kathryn P Lowry2, Nathan M Cross3

  • 1Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington.

Journal of the American College of Radiology : JACR
|May 24, 2024
PubMed
概括
此摘要是机器生成的。

在临床使用之前,在本地评估人工智能 (AI) 模型至关重要. 本研究提出了强大的AI验证基础设施,以确保患者安全并改善医疗保健结果.

关键词:
人工智能的人工智能是人工智能.临床实施 临床实施深度学习是一种深度学习.通过外部验证.放射科医生的工作流程

更多相关视频

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.5K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

824

相关实验视频

Last Updated: Jun 19, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.5K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

824

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 临床验证 临床验证

背景情况:

  • 获得FDA批准的人工智能 (AI) 算法在临床整合之前需要进行本地评估.
  • 确保AI的准确性和通用性对于患者安全和健康公平至关重要.
  • 数据隐私和知识产权等挑战阻碍了外部AI验证.

研究的目的:

  • 为人工智能模型开发高效,可定制和具有成本效益的外部验证基础设施提出解决方案.
  • 概述在临床系统之外建立人工智能推断基础设施的步骤,用于本地绩效评估.
  • 促进基于证据的方法,在医疗保健中采用人工智能模型.

主要方法:

  • 开发人工智能模型的外部验证基础设施.
  • 建立与临床系统分开的AI推断基础设施.
  • 在实施之前检查AI算法的本地性能.

主要成果:

  • 建议的策略解决了人工智能模型验证中的挑战.
  • 介绍了当地AI绩效评估的框架.
  • 这种方法促进了基于证据的AI采用.

结论:

  • 强大的本地验证基础设施对于安全和公平的AI整合到医疗保健中至关重要.
  • 外部验证框架可以克服数据隐私和知识产权方面的担忧.
  • 实施这些基础设施可以提高放射学工作流程和患者的治疗结果.