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

Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules02:34

Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules

37.4K
The test of the kinetic molecular theory (KMT) and its postulates is its ability to explain and describe the behavior of a gas. The various gas laws (Boyle’s, Charles’s, Gay-Lussac’s, Avogadro’s, and Dalton’s laws) can be derived from the assumptions of the KMT, which have led chemists to believe that the assumptions of the theory accurately represent the properties of gas molecules.
37.4K
Nephrotic Syndrome II : Assessment and Medical Management01:26

Nephrotic Syndrome II : Assessment and Medical Management

237
IntroductionNephrotic syndrome is a kidney disorder marked by excessive protein loss in the urine, leading to various systemic complications. This condition often results from damage to the glomeruli—the kidney's filtering units—causing proteinuria, low blood protein levels, and fluid retention. Understanding the assessment, diagnosis, and management of nephrotic syndrome is essential for effective treatment and prevention of further kidney damage.AssessmentPatient History: Document...
237
Irritable Bowel Syndrome II: Clinical Features and Diagnostic Evaluation01:30

Irritable Bowel Syndrome II: Clinical Features and Diagnostic Evaluation

768
Irritable Bowel Syndrome II: Clinical Features and Diagnostic Evaluation
Irritable Bowel Syndrome (IBS) is classified into subtypes based on the predominant bowel habits as determined by the Bristol Stool Form Scale (BSFS). The subtypes are:
768
Random and Systematic Errors01:20

Random and Systematic Errors

14.8K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.8K
Systematic Sampling Method01:17

Systematic Sampling Method

13.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
13.0K
Inhaled Medications01:23

Inhaled Medications

786
Inhaled medications are crucial for managing chronic obstructive pulmonary disease (COPD) and asthma. They are essential for effective treatment and control, ensuring optimal respiratory health and well-being. Inhaled medication delivers drugs directly to the lungs, providing a rapid onset of action and reducing systemic side effects compared to oral or injectable medications. Three primary types of inhalation devices are used to administer these medications: nebulizers, metered-dose inhalers...
786

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

Detection of Confounders and Potential Confounders in Computed Tomography Lung Datasets.

Journal of imaging informatics in medicine·2025
Same author

Evaluation of monocular and binocular contrast perception on virtual reality head-mounted displays.

Journal of medical imaging (Bellingham, Wash.)·2024
Same author

Accurate Neonatal Face Detection for Improved Pain Classification in the Challenging NICU Setting.

IEEE access : practical innovations, open solutions·2024
Same author

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms.

Bioengineering (Basel, Switzerland)·2024
Same author

Activated long-term memory and visual working memory during hybrid visual search: Effects on target memory search and distractor memory.

Memory & cognition·2024
Same author

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images.

PLOS digital health·2024

相关实验视频

Updated: Jan 29, 2026

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

1.4K

评估可解释性:医疗成像中可解释的人工智能特征的系统评估框架

Miguel A Lago1, Ghada Zamzmi1, Brandon Eich1

  • 1Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
概括

我们开发了一个框架来评估医疗成像中人工智能 (AI) 的解释性. 该系统使用一致性,可信性,准确性和有用性标准来量化解释质量,以获得更好的人工智能辅助诊断.

关键词:
人工智能的人工智能是人工智能.可以解释性的解释性.热图 热图 热图 热图可以解释的解释性.医学成像医学成像透明度 透明度 透明度

更多相关视频

Systematic Assessment of Well-Being in Mice for Procedures Using General Anesthesia
06:50

Systematic Assessment of Well-Being in Mice for Procedures Using General Anesthesia

Published on: March 20, 2018

12.9K
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

14.8K

相关实验视频

Last Updated: Jan 29, 2026

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

1.4K
Systematic Assessment of Well-Being in Mice for Procedures Using General Anesthesia
06:50

Systematic Assessment of Well-Being in Mice for Procedures Using General Anesthesia

Published on: March 20, 2018

12.9K
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

14.8K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 可解释的人工智能

背景情况:

  • 人工智能设备中的可解释性功能为内部机制提供了洞察力.
  • 目前对人工智能解释的评估技术缺乏,特别是在医学成像中.
  • 需要强大的方法来评估医疗保健中人工智能生成的解释的质量.

研究的目的:

  • 提出一个全面的框架来评估和报告医疗图像中的可解释AI (XAI) 特性.
  • 建立可量化的标准来评估AI在医疗器械中提供的解释质量.
  • 开发医学成像中XAI方法的得分卡,以配合AI设备.

主要方法:

  • 开发了一个基于四个标准的评估框架:一致性,可信性,忠实性和有用性.
  • 定义一致性为对类似输入的解释的变化.
  • 将可信性定义为解释与基本真相的接近程度,将忠实性定义为与模型机制的一致性,并将实用性定义为对任务执行的影响.

主要成果:

  • 该框架提供了一种定量方法来评估医学成像中的AI解释质量.
  • 为了完整描述和评估XAI方法,开发了一个得分卡.
  • 使用Ablation CAM和 Eigen CAM绘制的热图评估用于乳腺病变检测的案例研究.

结论:

  • 拟议的框架建立了量化医疗人工智能设备解释质量的标准.
  • 这项工作解决了在医学成像中缺乏XAI的评估技术的问题.
  • 开发的得分表和标准有助于对AI在临床应用中的可解释性进行彻底评估.