Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ESR1 Y537S and D538G Mutations Drive Resistance to CDK4/6 Inhibitors in Estrogen Receptor-Positive Breast Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

Opinion: Big Data Elements Key to Medical Imaging Machine Learning Tool Development.

Journal of breast imaging·2024
Same author

Breast Cancer Disparity and Outcomes in Underserved Women.

Radiographics : a review publication of the Radiological Society of North America, Inc·2023
Same author

Imaging and Management of Fibroepithelial Lesions of the Breast: Radiologic-Pathologic Correlation.

Radiographics : a review publication of the Radiological Society of North America, Inc·2023
Same author

Efficacy and Quality-of-Life Following Involved Nodal Radiotherapy for Head and Neck Squamous Cell Carcinoma: The INRT-AIR Phase II Clinical Trial.

Clinical cancer research : an official journal of the American Association for Cancer Research·2023
Same author

Pleiotrophin drives a prometastatic immune niche in breast cancer.

The Journal of experimental medicine·2023

Related Experiment Video

Updated: Jun 29, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.5K

Detailed Image Data Quality and Cleaning Practices for Artificial Intelligence Tools for Breast Cancer.

Dolly Y Wu1, Yisheng V Fang2, Dat T Vo3

  • 1Volunteer Services, UT Southwestern Medical Center, Dallas, TX.

JCO Clinical Cancer Informatics
|March 29, 2024
PubMed
Summary
This summary is machine-generated.

Standardizing how image data is prepared enhances the accuracy and consistency of artificial intelligence (AI) diagnostic tools. This ensures reliable AI performance in medical applications.

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K
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

834

Related Experiment Videos

Last Updated: Jun 29, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.5K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K
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

834

Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Data science

Background:

  • Artificial intelligence (AI) diagnostic tools require high-quality, consistent data for reliable performance.
  • Current image data preparation methods lack standardization, leading to variability in AI tool accuracy.

Purpose of the Study:

  • To establish standardized practices for preparing image data used in AI diagnostic tools.
  • To improve the accuracy and consistency of AI-driven diagnostic outcomes.

Main Methods:

  • Reviewing existing image data preparation protocols.
  • Developing a framework for standardized data preprocessing steps.
  • Implementing and validating the standardized approach on diverse datasets.

Main Results:

  • Demonstrated significant improvements in AI model accuracy post-standardization.
  • Reduced variability in diagnostic tool performance across different data sources.
  • Quantified the impact of standardized preparation on AI reliability.

Conclusions:

  • Standardized image data preparation is crucial for developing trustworthy AI diagnostic tools.
  • Adoption of these practices can enhance clinical utility and patient safety.
  • Further research should focus on domain-specific standardization protocols.