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 Experiment Videos

Rethinking Clinical Trial Radiology Workflows and Student Training: Integrated Virtual Student Shadowing Experience,

Lillian G Spear1,2, Jane A Dimperio3,4, Sherry S Wang5

  • 1NIH CC Radiology and Imaging Sciences, Bethesda, MD, USA. lillispear@verizon.net.

Journal of Digital Imaging
|February 23, 2022
PubMed
Summary

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

Considerations for Pre-deployment Planning in Point-of-Care Ultrasound Program Implementation: A HIMSS-SIIM Enterprise Imaging Community Whitepaper in Collaboration with AIUM.

Journal of imaging informatics in medicine·2026
Same author

ACR Appropriateness Criteria® Epigastric Pain.

Journal of the American College of Radiology : JACR·2026
Same author

Patient-Friendly Summary of the ACR Appropriateness Criteria®: Staging and Follow-Up of Pancreatic Neuroendocrine Tumors.

Journal of the American College of Radiology : JACR·2026
Same author

Patient-Friendly Summary of the ACR Appropriateness Criteria®: Staging and Follow-Up of Leukemia.

Journal of the American College of Radiology : JACR·2026
Same author

Reflections, Rest, and Renewal: A Year-End Conversation on Wellness-From Training to Practice, an <i>AJR</i> Podcast Series (Episode 6).

AJR. American journal of roentgenology·2025
Same author

Patient-Friendly Summary of the ACR Appropriateness Criteria®: Colorectal Cancer Screening: 2025 Update.

Journal of the American College of Radiology : JACR·2025
Same journal

Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios.

Journal of digital imaging·2023
Same journal

Detecting and Characterizing Inferior Vena Cava Filters on Abdominal Computed Tomography with Data-Driven Computational Frameworks.

Journal of digital imaging·2023
Same journal

DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.

Journal of digital imaging·2023
Same journal

Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.

Journal of digital imaging·2023
Same journal

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

Journal of digital imaging·2023
Same journal

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer.

Journal of digital imaging·2023
See all related articles
This summary is machine-generated.

This study introduces an online radiology curriculum for students, enhancing clinical exposure during the pandemic. The virtual training improves lesion identification and quantification in clinical trials, benefiting both students and radiologists.

Area of Science:

  • Medical Education
  • Radiology
  • Clinical Trials

Background:

  • The COVID-19 pandemic limited clinical exposure opportunities for radiology students.
  • There's a growing need for accurate lesion quantification in clinical trial radiology reports.

Purpose of the Study:

  • To present an online educational curriculum for radiology preprocessors (RPs).
  • To address the reduced availability of clinical radiology experience for students.
  • To improve the efficiency and accuracy of quantitative reporting in clinical trials.

Main Methods:

  • Developed a publicly available online curriculum on cross-sectional anatomy and advanced PACS tools.
  • Transitioned RPs to remote work and virtual training.
  • Assessed educational effectiveness through anatomical quizzes and monitoring RP miss rates.
Keywords:
Artificial intelligenceComputed tomographyMedical educationTumor quantification

Related Experiment Videos

  • Facilitated collaborations between academic institutions and industry.

    • Main Results:

    • The curriculum generated significant interest from multiple academic and research institutes.
    • A decrease in RP miss rates was observed over time, indicating improved accuracy.
    • Training effectiveness was demonstrated through reduced discrepancies with radiologist reports and enhanced tumor identification.

    Conclusions:

    • The virtual RP curriculum provides valuable clinical radiology experience in a remote setting.
    • This model enhances patient care through more accurate quantitative reports and improved radiologist efficiency.
    • The approach serves as a supplement to traditional education and a model for AI integration in radiology.