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Radiology CME

Free Radiology CME Courses Online

 Since 2010, vRad Radiology Education has offered high-quality continuing medical education (CME) modules on practical radiology topics—accessible to all radiologists and designed for convenient learning.

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Expired courses that are no longer available for CME credit are removed from this page. To view all course recordings (current and expired), please visit the vRad Radiology Education YouTube Channel.

Latest Courses

Bharti Khurana, MD, MBA, FACR, FASER

National Academy of Medicine Scholar in Diagnostic Excellence Founder and Director, Trauma Imaging Research and Innovation Center (TIRIC) Emergency Radiologist, Department of Radiology Research Scientist, Department of Medicine Associate Professor of Radiology, Harvard Medical School Brigham and Women's Hospital

Intimate Partner Violence

0.25 AMA PRA Category 1 Credit™

Cost: Free

Expires: 12/31/2027

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Course Overview

The course focuses on Intimate Partner Violence (IPV), a highly prevalent yet often disregarded public health issue. Despite radiologists' pivotal role in identifying non-accidental trauma in children, our training often overlooks the nuances of detecting abuse in adults, where significant underreporting persists due to societal stigma and interdependency dynamics. 

Learning Objectives


1. Intimate partner violence (IPV) is a highly prevalent public health issue with multiple adverse health effects. Radiologists are well suited to assessing a patient’s likelihood of IPV. 

2. Recognition of common IPV injury mechanisms and resulting target and defensive injury patterns on imaging and understanding of differences between patients who have experienced IPV and those who have not with respect to use of imaging will aid radiologists in accurate IPV diagnosis. 

3. Awareness of differences in injury patterns resulting from IPV-related and accidental trauma can aid radiologists in detecting a mismatch between the provided clinical history and imaging findings to support suspicion of IPV. Radiologists should consider all available current and prior imaging in assessing the likelihood of IPV; this process may be aided by machine learning methods.

In: General