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

You might also read

Related Articles

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

Sort by

Same author

ACR Appropriateness Criteria® Breast Imaging During Lactation.

Journal of the American College of Radiology : JACR·2026

Same author

A RRA Perspective on AI and Machine Learning Applications in Radiology: From Experimental to Clinically Viable Solutions.

Academic radiology·2026

Same author

Teleradiology, Edge Computing, and the Internet of Things in Radiology: A RRA Perspective on Decentralized Diagnostics.

Academic radiology·2026

Same author

Patient perspectives on artificial intelligence in mammography interpretation: a comparative survey study of safety-net and academic hospital settings.

Breast cancer research and treatment·2025

Same author

ACR Appropriateness Criteria® Breast Imaging During Pregnancy.

Journal of the American College of Radiology : JACR·2025

Same author

Extramammary metastases to the breast: Radiologic and histologic review.

Clinical imaging·2025

Same journal

Preoperative breast MRI and long-term survival in ductal carcinoma in situ: a propensity score-weighted analysis.

European radiology·2026

Same journal

Efficacy evaluation of artificial intelligence in radiological imaging diagnosis based on randomized controlled trials: a scoping review.

European radiology·2026

Same journal

The bMRI-QUAL scoring system: an important first step toward standardizing breast MRI quality.

European radiology·2026

Same journal

Spectral CT-based habitat analysis for predicting pathologic response to neoadjuvant therapy in gastric cancer.

European radiology·2026

Same journal

MR-guided microwave ablation of liver tumors: outcomes in local tumor control and determinants of treatment success.

European radiology·2026

Same journal

AI integration in pediatric radiology: perspectives from international academic leaders.

European radiology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 21, 2025

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

Deep learning in breast radiology: current progress and future directions.

William C Ou¹, Dogan Polat², Basak E Dogan²

¹Department of Radiology, Seay Biomedical Building, University of Texas Southwestern Medical Center, 2201 Inwood Road, Dallas, TX, 75390, USA. William.Ou@UTSouthwestern.edu.

European Radiology

|January 15, 2021

Summary

This summary is machine-generated.

Deep learning (DL) enhances artificial intelligence (AI) in breast radiology for improved lesion detection and classification. Future applications may include risk estimation and therapy response prediction, though data and medicolegal challenges remain.

Keywords:

Artificial intelligence Breast magnetic resonance imaging Breast ultrasonography Deep learning Digital mammography

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Nov 21, 2025

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

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Deep learning (DL) is rapidly advancing within breast radiology.
Artificial intelligence (AI) offers distinct advantages over traditional computer-aided detection methods.
DL applications show potential to significantly augment radiologist capabilities.

Purpose of the Study:

To review current applications of deep learning in breast radiology.
To explore the potential of DL in enhancing diagnostic accuracy and clinical decision-making.
To identify remaining challenges for DL implementation in breast imaging.

Main Methods:

Review of current literature on deep learning applications in breast radiology.
Analysis of DL's role in detection, classification, risk estimation, and therapy response prediction.
Identification of key challenges and future directions for DL in breast imaging.

Main Results:

Deep learning methods are improving diagnostic capabilities in breast radiology.
DL has the potential to improve diagnostic accuracy, efficiency, and clinical decision-making.
DL can aid in predicting prognosis and patient response to therapy.

Conclusions:

Deep learning is a transformative technology in breast radiology.
Further research is needed to address data limitations and medicolegal issues.
Successful integration of DL requires high-quality data and robust validation.