Jove
Visualize
Contact Us

Related Concept Videos

Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

962
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...
962
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

445
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...
445

You might also read

Related Articles

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

Sort by
Same author

The albino mutation of tyrosinase alters ocular angiogenic responsiveness.

Angiogenesis·2013
Same journal

History of MSK Section of the Italian Society of Radiology.

Seminars in musculoskeletal radiology·2026
Same journal

Principles of Anatomy and Function in Wrist Imaging.

Seminars in musculoskeletal radiology·2026
Same journal

Opportunistic Screening Based on Computed Tomography in Musculoskeletal Radiology: How and Why.

Seminars in musculoskeletal radiology·2026
Same journal

Musculoskeletal Computed Tomography Imaging: A 30-Year Perspective.

Seminars in musculoskeletal radiology·2026
Same journal

Current Advances and Controversies in Spine Imaging.

Seminars in musculoskeletal radiology·2026
Same journal

New Techniques in Musculoskeletal MRI: State of the Art.

Seminars in musculoskeletal radiology·2026
See all related articles
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 Experiment Video

Updated: Dec 29, 2025

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

700

Artificial Intelligence in Radiology Residency Training.

Michael C Forney1,2, Aaron F McBride1

  • 1Section of Musculoskeletal Imaging, Imaging Institute, Cleveland Clinic, Cleveland, Ohio.

Seminars in Musculoskeletal Radiology
|January 29, 2020
PubMed
Summary
This summary is machine-generated.

This article examines how radiology residency programs must adapt to incorporate artificial intelligence training. It outlines the essential knowledge, assessment skills, and resources needed to prepare future radiologists for the integration of these technologies into clinical practice.

Keywords:
medical educationdiagnostic imagingclinical trainingdigital health

Frequently Asked Questions

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
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Related Experiment Videos

Last Updated: Dec 29, 2025

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

700
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
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Area of Science:

  • Medical education research within Artificial Intelligence in radiology
  • Radiological sciences and clinical training pedagogy

Background:

The rapid evolution of digital diagnostic tools has created a significant knowledge gap in current medical education curricula. Prior research has shown that existing training frameworks often lack structured guidance on emerging computational technologies. That uncertainty drove a need for updated pedagogical strategies to ensure future clinicians remain proficient. No prior work had resolved how residency programs should systematically integrate these advanced systems. This gap motivated a re-evaluation of standard teaching objectives for diagnostic imaging trainees. Scholars have increasingly highlighted the disparity between technological advancement and formal instruction. Educators now face the challenge of aligning residency requirements with the demands of modern healthcare environments. Addressing this disconnect remains a priority for academic institutions worldwide.

Purpose Of The Study:

The aim of this study is to outline the necessary components for integrating computational intelligence into radiology residency programs. The authors seek to address the lack of formal guidance for training future clinicians in these emerging fields. This work explores how residency directors can prepare trainees for the technological shifts expected in the coming decades. The researchers intend to define the foundational knowledge required for effective clinical practice. This study addresses the specific challenge of aligning traditional medical education with rapid advancements in diagnostic software. The authors aim to provide a framework for assessing the utility of these new tools in clinical environments. This research also identifies the types of resources needed to support ongoing learning for residents. The study serves as a guide for academic programs looking to modernize their instructional objectives.

Main Methods:

Review Approach involved a systematic synthesis of current pedagogical requirements for diagnostic imaging specialists. The investigation utilized a comprehensive analysis of existing educational literature to identify necessary curriculum updates. Researchers examined the intersection of computational advancements and standard clinical instruction protocols. This process focused on defining the core competencies required for future medical professionals. The study design prioritized identifying gaps in current training models through a structured literature review. Investigators evaluated various frameworks for teaching complex diagnostic systems to trainees. This approach ensured that all recommendations were grounded in established academic standards. The methodology provided a clear roadmap for implementing these changes within existing residency structures.

Main Results:

Key Findings From the Literature indicate that residency programs should prioritize four distinct areas of instruction. The authors report that trainees must gain a firm understanding of the fundamental principles behind automated diagnostic systems. Evidence shows that residents need to be familiar with the broad range of clinical applications for these technologies. The literature suggests that learning how to critically assess software performance is a vital skill for future radiologists. Findings highlight that access to specialized educational resources is necessary for building long-term knowledge. The study indicates that these components are essential for preparing residents for the future of their careers. Data suggest that a comprehensive approach to training will improve the adaptability of future clinicians. The researchers emphasize that these four pillars should form the basis of updated residency curricula.

Conclusions:

Synthesis and Implications suggest that residency programs must prioritize the inclusion of computational intelligence within their core curricula. The authors propose that trainees require a robust grasp of foundational concepts to navigate future clinical landscapes. Evidence indicates that understanding the diverse applications of these systems is necessary for effective diagnostic practice. The researchers suggest that residents should develop specific skills to critically evaluate the performance of automated tools. This review implies that access to curated educational resources will support ongoing professional development for radiologists. The authors maintain that preparing for these shifts will define the success of future medical practitioners. Synthesis and Implications highlight that proactive curriculum updates are required to maintain high standards of patient care. The researchers conclude that integrating these topics will ensure residents are ready for the evolving demands of their profession.

The authors propose that residency programs should teach residents how to evaluate the performance of automated tools. This skill is necessary to ensure that clinicians can effectively interpret and validate diagnostic suggestions provided by computational systems in a clinical setting.

The researchers identify foundational knowledge of system types as a key component. This includes understanding the underlying architecture of diagnostic software, which differs from traditional image interpretation methods used in standard residency training programs.

The authors argue that a structured curriculum is necessary because the rapid pace of technological innovation outstrips traditional learning models. This approach allows programs to standardize the delivery of complex information across different training sites.

The researchers suggest that residents utilize curated educational resources to build their expertise. These materials serve as a bridge between theoretical understanding and the practical application of automated diagnostic systems in real-world clinical scenarios.

The authors emphasize the measurement of clinical competency through the assessment of automated applications. This phenomenon involves testing whether a resident can accurately identify the strengths and limitations of specific software outputs during diagnostic tasks.

The researchers propose that residency programs must adapt to these changes to remain relevant. They claim that failing to integrate these topics will leave future radiologists unprepared for the technological shifts expected in the coming decades.