AJR Podcasts AJR

In this episode of the AJR Podcast Series on Training & Education, Pamela Schaefer joins host Monica Cheng to discuss incorporating education into radiology careers. Dr. Schaefer shares her journey, the role of leadership, and advice for aspiring educators.

In the AJR Podcast Series on Training and Education, Dr. Monica Cheng, who is a radiology resident at Massachusetts General Hospital and Consulting Editor for the AJR Podcast Series, delves into essential topics within the realm of training and education with some of radiology's brightest thinkers, innovators, and leaders. Her guests’ stories, ideas, and lived experiences shine a fresh light on the way we think about current and evolving educational paradigms, the foundational values of training, and the culture of learning and growth. These form the pillars on which we deliver patient care in a dynamic clinical landscape and continue serving the evolving needs of healthcare and society.
 

In this AJR Conversation, Musculoskeletal Imaging Section Editor Eric Y. Chang, MD speaks with Eddy D. Zandee van Rilland, MD about his team's recently published article reviewing the fundamental features of myositis, including recent updates in terminology and consensus guidelines for idiopathic inflammatory myopathies.

Rebecca Le, MD discusses an article detailing how core needle biopsy may help avoid a missed or delayed cancer diagnosis in pulmonary lesions with cystic airspaces.
ARTICLE TITLE - CT-Guided Core-Needle Biopsy of Pulmonary Lesions Associated With Cystic Airspaces: A Case-Control Study

In this episode of the AJR Podcast Series on Wellness, Sherry Wang, MBBS discusses how loneliness and social connectedness impact wellbeing and how we can connect with others to build meaningful relationships. These issues are important in view of the continued growth of teleradiology.

In this podcast, Shruti Kumar, MD, discusses the study by Somasundaram et al. on the development and validation of deep-learning models for abdominal CT organ segmentation in children.
ARTICLE TITLE - Deep-Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets