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

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

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

Sort by
Same author

Follow the data: tracking data quality and completeness in oncology real-world data.

JAMIA open·2026
Same author

An agentic AI system enhances clinical detection of immunotherapy toxicities: a multi-phase validation study.

medRxiv : the preprint server for health sciences·2026
Same author

Evaluation of electronic health record to HL7® FHIR® mappings in pediatric research studies.

International journal of medical informatics·2026
Same author

Dynamic Contrast-enhanced MRI for Evaluating Breast Cancer Chemotherapy Response Using Conditional Generative Adversarial Networks.

Radiology. Imaging cancer·2025
Same author

Extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model for Critical Care Medicine: A Framework for Standardizing Complex ICU Data Using the Society of Critical Care Medicine's Critical Care Data Dictionary (C2D2).

Critical care medicine·2025
Same author

Leveraging the Rural-Urban Commuting Area Tool to Address Geographic Disparities in Cancer Care: A Dual-Application Framework for Institutional and National Initiatives.

JCO clinical cancer informatics·2025

Related Experiment Video

Updated: May 11, 2026

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

Improving Equity in Deep Learning Medical Applications with the Gerchberg-Saxton Algorithm.

Seha Ay1, Michael Cardei2, Anne-Marie Meyer3

  • 1Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC USA.

Journal of Healthcare Informatics Research
|April 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bias reduction method for deep learning (DL) in healthcare, using frequency domain transformation to mitigate racial and ethnic disparities in patient outcome predictions.

Keywords:
Deep learningMIMIC-IIIMedical decision-makingMortality rate predictionRacial bias mitigation

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

393

Related Experiment Videos

Last Updated: May 11, 2026

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
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

393

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Statistical Modeling

Background:

  • Deep learning (DL) shows promise in healthcare for diagnosis and prognosis.
  • However, DL models can amplify existing biases due to data collection and practice variability.
  • Selection bias particularly affects underrepresented populations, leading to inaccurate and inequitable outcomes.

Purpose of the Study:

  • To investigate a novel method for reducing bias in deep learning models.
  • To address the impact of bias on the generalizability and accuracy of DL healthcare applications.
  • To specifically examine the influence of racio-ethnic bias on model outcomes.

Main Methods:

  • The study employed frequency domain transformation techniques.
  • Specifically, the Gerchberg-Saxton algorithm was utilized for bias reduction.
  • The impact of this method on racio-ethnic bias was analyzed.

Main Results:

  • The proposed method demonstrated potential in mitigating bias within DL models.
  • Frequency domain transformation showed a positive impact on reducing racio-ethnic disparities.
  • This approach offers a pathway to more equitable AI in healthcare.

Conclusions:

  • Addressing bias is crucial for the safe and effective implementation of DL in healthcare.
  • Novel methods like frequency domain transformation can improve fairness and reduce harm.
  • Further research is needed to validate and refine these bias reduction techniques for diverse populations.