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

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.1K
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...
6.1K
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

977
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...
977
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

729
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
729
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

466
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...
466
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

433
The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
433

You might also read

Related Articles

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

Sort by
Same author

Transplant-ready? Evaluating AI lung segmentation models in candidates with severe lung disease.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

Deep learning automates Cobb angle measurement compared with multi-expert observers.

BJR artificial intelligence·2026
Same author

The impact of scanner domain shift on deep learning performance in medical imaging: an experimental study.

International journal of computer assisted radiology and surgery·2026
Same author

Rethinking Pulmonary Embolism Segmentation: A Study of Current Approaches and Challenges with an Open Weight Model.

Journal of imaging informatics in medicine·2026
Same author

Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.

Medical image analysis·2026
Same author

Segment Anything Model 2: An Application to 2D and 3D Medical Images.

IEEE transactions on bio-medical engineering·2026
Same journal

Development and Validation of an Interpretable Model Integrating Radiomics and Clinical Data for Predicting 70-gene Signature Risk in Breast Cancer: A Multicenter Study.

Academic radiology·2026
Same journal

AI-Based Longitudinal Scoliosis Monitoring on EOS Whole-Spine Radiographs in a Pediatric to Young Adult Cohort.

Academic radiology·2026
Same journal

Energy Conservation in MRI: Sequence Selection and Operational Strategies.

Academic radiology·2026
Same journal

Improving Reliability of MRI Lumbar Spinal Stenosis Assessment Across Radiology and Spine Specialties: Impact of a Structured Education Intervention.

Academic radiology·2026
Same journal

Advances in CT and MRI for Yttrium-90 Radioembolization of Hepatocellular Carcinoma.

Academic radiology·2026
Same journal

Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated Steatohepatitis.

Academic radiology·2026
See all related articles

Related Experiment Video

Updated: Jan 2, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

721

Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers.

Maciej A Mazurowski1

  • 1Departments of Radiology, Electrical and Computer Engineering, and Biostatistics and Bioinformatics, Duke University, 2424 Erwin Rd, Durham, NC 27707.

Academic Radiology
|December 11, 2019
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in radiology presents potential conflicts between professional self-interests and patient well-being. Responsible research and implementation are crucial to ensure AI benefits patients most.

Keywords:
Algorithm developmentArtificial intelligenceEthicsMachine learning

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

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

Published on: February 23, 2024

1.3K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.8K

Related Experiment Videos

Last Updated: Jan 2, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

721
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

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

Published on: February 23, 2024

1.3K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.8K

Area of Science:

  • Radiology
  • Medical Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly integrated into radiology.
  • Potential conflicts exist between the self-interests of radiologists and AI developers and the best interests of patients.

Purpose of the Study:

  • To examine the potential misalignment of self-interests between radiologists, AI developers, and patient well-being.
  • To highlight the need for responsible AI research and clinical implementation in radiology.

Main Methods:

  • Conceptual analysis of motivations and incentives for radiologists and AI developers.
  • Discussion of the impact of these incentives on AI research and clinical adoption.

Main Results:

  • Radiologists may be incentivized to oppose AI due to concerns about employment and professional standing.
  • AI developers may be incentivized to overstate AI capabilities, potentially eroding trust.
  • The self-interests of stakeholders can diverge from optimal patient outcomes.

Conclusions:

  • Recognizing personal motivations and biases is essential for radiologists and AI researchers.
  • Responsible conduct is paramount to ensure AI transformation in radiology maximally benefits patients.