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

Bias01:22

Bias

4.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.2K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

254
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
254
Brain Imaging01:14

Brain Imaging

228
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
228
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.1K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
5.1K

You might also read

Related Articles

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

Sort by
Same author

Reply to "Artificial Intelligence in Radiology: The Paradox Between Efficiency and Burnout".

AJR. American journal of roentgenology·2026
Same author

Radiology Reimagined: Interoperability and Lessons Learned from the Imaging AI in Practice Demonstration.

Radiology·2026
Same author

Automated right ventricle-to-left ventricle diameter ratio predicts ICU stay for acute pulmonary embolism on CTPA examinations in the emergency department.

Cardiovascular diagnosis and therapy·2026
Same author

Artificial Intelligence Sees the Image, Radiologists See the Patient.

AJR. American journal of roentgenology·2026
Same author

Reporting checklist for foundation and large language models in medical research (REFINE): an international consensus guideline.

Diagnostic and interventional radiology (Ankara, Turkey)·2026
Same author

Agentic AI in Radiology: Evolution from Large Language Models to Future Clinical Integration.

Radiology. Artificial intelligence·2026
Same journal

MRI of Lesions Growing Along the Pituitary Stalk.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same journal

Invited Commentary: Early Detection of Pancreatic Cancer: Are We Up for the Challenge?

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same journal

Radiology Board Examinations: A Fundamental Shift.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same journal

Early Pancreatic Cancer: Clinical Implications, Workup, and Imaging Findings with Histopathologic Correlation for Personalized Surveillance.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same journal

Comprehensive Approach to Prostate Cancer Metastasis Mimics at Prostate-Specific Membrane Antigen PET/CT.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same journal

Invited Commentary: Postdeployment Monitoring of AI in Radiology: Beyond the Test Set.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2025

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

Understanding and Mitigating Bias in Imaging Artificial Intelligence.

Ali S Tejani1, Yee Seng Ng1, Yin Xi1

  • 1From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390.

Radiographics : a Review Publication of the Radiological Society of North America, Inc
|April 18, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) bias in medical imaging can harm patients and worsen health inequities. Understanding AI bias sources and impacts is crucial for radiologists to implement quality control and ensure equitable AI tool development and use.

More Related Videos

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Related Experiment Videos

Last Updated: Jun 28, 2025

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
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.5K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Area of Science:

  • Medical imaging
  • Artificial intelligence
  • Health equity

Background:

  • Artificial intelligence (AI) algorithms are susceptible to bias throughout development, potentially worsening health disparities.
  • Bias in imaging AI is multifaceted, encompassing unequal preferences, cognitive deviations, and statistical prediction errors.
  • Biased AI models can lead to patient harm and exacerbate health inequities due to differential performance across populations.

Purpose of the Study:

  • To clarify definitions of bias in artificial intelligence (AI).
  • To identify common sources of bias in the imaging machine learning lifecycle.
  • To provide recommendations for quality control measures to mitigate bias in imaging AI.

Main Methods:

  • Review of definitions of bias in AI, including unequal preference, cognitive bias, and statistical bias.
  • Analysis of bias sources across the imaging machine learning lifecycle.
  • Simplification of technical terminology for general radiologists and AI developers.

Main Results:

  • AI bias can manifest as unequal preferences, cognitive deviations, or statistical errors, impacting clinical decisions.
  • Biased AI models can lead to inaccurate results, patient harm, and health inequities.
  • Awareness of post-deployment biases like automation bias is also critical for radiologists.

Conclusions:

  • Understanding the diverse definitions and sources of imaging AI bias is essential for proactive mitigation.
  • Implementing quality control measures can help address underrepresentation and reduce the impact of bias.
  • A mindful approach to AI development and deployment is necessary to ensure equitable outcomes in healthcare.