Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

Non-equilibrium in the Cell

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

You might also read

Related Articles

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

Sort by

Same author

Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

JMIR formative research·2024

Same author

Extracting factors associated with vaccination from Twitter data and mapping to behavioral models.

Human vaccines & immunotherapeutics·2023

Same author

Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review.

JMIR medical informatics·2023

Same author

Feature selection enhancement and feature space visualization for speech-based emotion recognition.

PeerJ. Computer science·2022

Same author

A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset.

Sensors (Basel, Switzerland)·2022

Same author

Influences of social media usage on public attitudes and behavior toward COVID-19 vaccine in the Arab world.

Human vaccines & immunotherapeutics·2022

Same journal

Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study.

JMIR medical informatics·2026

Same journal

IdeaDistiller-AI Support for Idea Synthesis in Concept Mapping: Algorithm Development and Validation Study.

JMIR medical informatics·2026

Same journal

Pregnancy-Related Clinical Codes in Unlikely Populations in Primary Care.

JMIR medical informatics·2026

Same journal

Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.

JMIR medical informatics·2026

Same journal

Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China.

JMIR medical informatics·2026

Same journal

Assessing the Feasibility and Acceptability of Implementing a Preclinic Vital Signs Assessment in Primary Care: Cross-Sectional Pilot Study.

JMIR medical informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 7, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping

Hazrat Ali¹, Zubair Shah¹

¹College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

JMIR Medical Informatics

|June 16, 2022

Summary

This summary is machine-generated.

Generative adversarial networks (GANs) address COVID-19 data scarcity in lung imaging, enhancing AI diagnosis through data augmentation, segmentation, and superresolution. GANs show potential for clinical applications, improving convolutional neural network (CNN) model performance.

Keywords:

COVID-19 CT scan X-ray artificial intelligence augmentation clinical informatics data augmentation data scarcity diagnosis diagnostic generative adversarial networks image data imaging lung image neural network

More Related Videos

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

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

Published on: November 30, 2022

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

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

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Sep 7, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

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

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

Published on: November 30, 2022

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

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

Published on: February 23, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Data Science

Background:

COVID-19 diagnosis using lung images is hindered by limited data.
Generative adversarial networks (GANs) are effective for data synthesis and augmentation.
GANs show promise for improving AI performance in COVID-19 diagnosis from lung CT and X-ray images.

Purpose of the Study:

To comprehensively review the role of GANs in addressing COVID-19 data scarcity and diagnosis.
To summarize GAN methods, lung imaging datasets, popular architectures, modalities, and code availability for COVID-19.
To answer key questions regarding GAN applications in COVID-19 diagnosis.

Main Methods:

Systematic search of 5 databases (PubMed, IEEEXplore, ACM, Scopus, Google Scholar) from October 11-13, 2021.
Keywords included "generative adversarial networks" (GANs) and "COVID-19."
Included studies published 2020-2022, in English, focusing on GANs for chest X-ray, CT, or ultrasound images, following PRISMA-ScR guidelines.

Main Results:

57 studies utilized GANs for COVID-19 lung imaging; 74% used GANs for data augmentation.
CycleGAN and conditional GAN were most common architectures.
Chest X-ray (51%) and CT (37%) were primary modalities; 82% used public data, with limited radiologist evaluation (4%).

Conclusions:

GANs demonstrate significant potential to overcome data scarcity in COVID-19 lung imaging.
GAN-synthesized data improves convolutional neural network (CNN) training and performance for COVID-19 diagnosis.
GANs enhance CNNs via superresolution and segmentation, though clinical translation challenges remain.