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...

Radiological Investigation I: X-ray and CT

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...

Magnetic Resonance Imaging

Magnetic Resonance Imaging

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...

Radiological Investigation II: MRI and Ventilation Perfusion Scan

Radiological Investigation II: MRI and Ventilation Perfusion Scan

Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...

You might also read

Related Articles

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

Sort by

Same author

Sharpness matters: Higher image resolution improves generalization and explainability in chest X-ray classification.

Scientific reports·2026

Same author

Explainable Artificial Intelligence (AI) for Medical Imaging: A Framework for Bridging the AI Trust Gap.

AJR. American journal of roentgenology·2026

Same author

Portrayal of Medical Students in Artificial Intelligence- Generated Images.

WMJ : official publication of the State Medical Society of Wisconsin·2026

Same author

Increasing Use of Large Language Models for Scholarly Writing in Radiology.

AJR. American journal of roentgenology·2026

Same author

Clinical and Pathologic Description of Three Aneurysmal Bone Cyst Cases With Novel USP6 Fusion Partners Including SEC24D, HNRNPC, and ERRFI1.

Genes, chromosomes & cancer·2025

Same author

Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology.

Radiology·2025

Search research articles

Related Experiment Video

Updated: Sep 1, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Academic Radiology Departments Should Lead Artificial Intelligence Initiatives.

Samantha M Santomartino¹, Eliot Siegel¹, Paul H Yi²

¹University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, W. Baltimore Street, First Floor, Rm. 1172, 21201 Baltimore, MD.

Academic Radiology

|August 14, 2022

Summary

Radiologists can lead the AI era by integrating artificial intelligence (AI) into education, research, and clinical practice. This perspective offers guidance and examples for academic radiology departments to embrace AI and industry collaboration for future success.

Keywords:

Artificial intelligence radiology educational initiatives perspective

More Related Videos

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

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Related Experiment Videos

Last Updated: Sep 1, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

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

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Area of Science:

Radiology
Medical Artificial Intelligence (AI)

Background:

Radiologists possess unique skills and data access, positioning them to lead the integration of artificial intelligence (AI) in medicine.
Academic radiology departments must adapt to the evolving landscape of medical technology.

Purpose of the Study:

To outline key artificial intelligence (AI) initiatives for academic radiology departments.
To guide leaders in leveraging AI for educational, research, and clinical advancements.
To highlight opportunities for industry engagement in radiology AI development.

Main Methods:

This perspective piece summarizes current AI initiatives and provides strategic recommendations.
It draws upon existing successful examples within academic radiology.
The content is structured around traditional departmental pillars and new industry collaborations.

Main Results:

Early successful examples of AI integration in radiology education, research, and clinical practice are presented.
Actionable suggestions are provided to facilitate departmental adoption of AI.
The potential for synergistic partnerships between academic radiology and industry is explored.

Conclusions:

Academic radiology departments are well-positioned to pioneer the AI era.
Strategic implementation of AI initiatives is crucial for realizing the full potential of artificial intelligence in radiology.
Proactive engagement with AI and industry will enhance departmental excellence and patient care.