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...

You might also read

Related Articles

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

Sort by

Same author

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics.

Healthcare (Basel, Switzerland)·2024

Same author

Juvenile Idiopathic Arthritis: A Review of Novel Diagnostic and Monitoring Technologies.

Healthcare (Basel, Switzerland)·2021

Same author

Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive and Vestibular Systems to Balance in Young Healthy Adult Subjects.

Healthcare (Basel, Switzerland)·2021

Same author

Infrared thermography can detect previsual bacterial growth in a laboratory setting via metabolic heat detection.

Journal of applied microbiology·2021

Search research articles

Related Experiment Video

Updated: May 6, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.

Olamilekan Shobayo^1,2, Reza Saatchi¹

¹School of Engineering and Built Environment, Sheffield Hallam University, Pond Street, Sheffield S1 1WB, UK.

Diagnostics (Basel, Switzerland)

|May 14, 2025

Summary

This summary is machine-generated.

Deep learning significantly enhances medical image analysis for diagnostics. Key challenges include data, interpretability, and ethics, but advancements promise improved patient outcomes.

Keywords:

artificial intelligence artificial neural networks deep learning image classification and pattern recognition medical image analysis

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

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

Related Experiment Videos

Last Updated: May 6, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

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

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Diagnostic Technology

Background:

Deep learning (DL) revolutionizes medical image analysis, offering automated, efficient, and accurate diagnostic solutions.
Advancements in DL techniques are transforming the interpretation of medical imaging data across various modalities.

Purpose of the Study:

To explore recent developments in deep learning techniques for medical imaging.
To identify key challenges and future research directions for clinical adoption.

Main Methods:

Systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Searches conducted on PubMed, Google Scholar, and Scopus databases.
Exploration of various DL architectures including CNNs, RNNs, GANs, U-Nets, ViTs, and hybrid models.

Main Results:

Deep learning models show significant potential in enhancing diagnostic accuracy for MRI, CT, US, and X-ray.
Key challenges identified include data availability, interpretability, overfitting, computational demands, model trust, data privacy, and ethical considerations.
Various DL architectures are effective for classification, segmentation, feature extraction, and image synthesis.

Conclusions:

Deep learning offers substantial promise for improving diagnostic accuracy in medical imaging.
Addressing challenges in data, interpretability, ethics, and computational efficiency is crucial for broader clinical adoption.
Future research should focus on real-time applications, enhanced explainability, and integration into healthcare frameworks for better patient outcomes.