Connie Lehman, MD, chief of breast imaging, co-director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital, discusses how artificial intelligence (AI) is being implemented in breast imaging.

Children are at heightened risk of major clinical depression when at least one parent has a history of the disorder. New research shows depression markers appearing on structural and functional brain MRI ahead of symptoms in these “familial risk” offspring from infancy through early adulthood.

Prior studies have focused on radiologist performance rather than patient outcomes, leaving the topic of variable diagnoses and what factors impact them—race, ethnicity, age, biopsy type, etc.—open for debate. 

Low-dose scans showed high agreement with joint-by-joint rheumatological evaluations. 

The new findings may contain important implications for neuromodulation therapies to treat depression symptoms.

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, explains some of technology trends KLAS researchers have found in enterprise imaging system and radiology artificial intelligence (AI).

Charles Kahn, Jr., MD, editor of the the journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, explains the work involved integrating AI in radiology systems and the role of AI in augmenting patient care.
 

Charles E. Kahn, Jr., MD, MS, editor of the the RSNA  journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, discusses the need to validate AI algorithms with your own patient population data.