News | Artificial Intelligence | May 02, 2018

Charles E. Kahn Jr. Named Editor of Radiology: Artificial Intelligence

New journal launching in 2019 will highlight emerging applications of machine learning and artificial intelligence in radiology

Charles E. Kahn Jr. Named Editor of Radiology: Artificial Intelligence

May 2, 2018 – The Board of Directors of the Radiological Society of North America (RSNA) announced that Charles E. Kahn Jr., M.D., M.S., will become editor of the new online journal, Radiology: Artificial Intelligence. The new journal will highlight emerging applications of machine learning and artificial intelligence (AI) in the field of imaging across multiple disciplines. It will provide a way to keep practicing physicians and imaging researchers up to date on the best emerging science in this subspecialty.

Beginning in early 2019, the journal will be published bi-monthly and available exclusively online. RSNA members will receive a complimentary subscription as a member benefit. Original research and editorial submissions to Radiology: Artificial Intelligence will be accepted beginning summer 2018.

Kahn is professor and vice chair of the Department of Radiology at the University of Pennsylvania’s Perelman School of Medicine in Philadelphia since joining the university in 2014. He is also a senior fellow of the Institute for Biomedical Informatics and the Leonard Davis Institute of Health Economics at Penn.

Radiology: Artificial Intelligence is an exciting venture,” Kahn said. “I’m deeply honored for the opportunity to serve as this new journal’s editor, and look forward to working with our authors, reviewers and editorial team to present cutting-edge science in this rapidly growing field.”

A board-certified radiologist with a clinical specialty in abdominal imaging, Kahn’s research interests include health services, comparative effectiveness, decision support, information standards and knowledge representation.

Kahn has served as an associate editor for the journal Radiology, and as an associate editor for the American Journal of Roentgenology (AJR). He has also served as a section editor for the Yearbook of Medical Informatics, a guest editor for the Journal of the American College of Radiology (JACR), and as chair of the Publications Committee of the American Roentgen Ray Society (ARRS).

He has been a reviewer for several scientific journals, including Radiology, RadioGraphics, AJR, Academic Radiology, Journal of the American Medical Informatics Association (JAMIA), Journal of Biomedical Informatics, Journal of Digital Imaging, European Radiology and Artificial Intelligence in Medicine.

Kahn has authored or co-authored more than 110 peer-reviewed articles, and given nearly 100 invited lectures.

“With his extensive editorial experience and profound interest in the fields of artificial intelligence and machine learning, Charles Kahn is a natural fit for this role,” said Mary C. Mahoney, M.D., RSNA board liaison for publications and communications. “The RSNA Board is excited to see where he takes this important new journal.”

For more information: www.rsna.org

 

Related Content

AI Could Use EKG Data to Measure Patient's Overall Health Status

Image courtesy of iStock

News | Artificial Intelligence | August 29, 2019
In the near future, doctors may be able to apply artificial intelligence (AI) to electrocardiogram data in order to...
Half of Hospital Decision Makers Plan to Invest in AI by 2021
News | Artificial Intelligence | August 08, 2019
A recent study conducted by Olive AI explores how hospital leaders are responding to the imperative to drive efficiency...
Artificial Intelligence Solution Improves Clinical Trial Recruitment

A nurse examines a patient in the Emergency Department of Cincinnati Children’s, where researchers successfully tested artificial intelligence-based technology to improve patient recruitment for clinical trials. Researchers report test results in the journal JMIR Medical Informatics. Image courtesy of Cincinnati Children’s.

News | Artificial Intelligence | July 31, 2019
Clinical trials are a critical tool for getting new treatments to people who need them, but research shows that...

An example of AI-assisted automation developed by TomTec, where a deep learning algorithm automatically marks the myocardial borders and performs auto quantification This removes time consuming tasks to free up the operator to spend more time with patients and helps make exams more reproducible.

Feature | Artificial Intelligence | July 26, 2019
Intelligent software solutions (aka...
vRad Presents AI Model to Assess Probability of Aortic Dissection
News | Artificial Intelligence | July 01, 2019
vRad (Virtual Radiologic), a Mednax company recently made a scientific presentation, “Screening for Aortic Dissection...
Videos | Artificial Intelligence | June 28, 2019
This is a quick example of how artificial intelligence (AI) is being integrated on the back end of cardiac ultrasound
Third FDA Clearance Announced for Zebra-Med's AI Solution for Brain Bleed Alerts
Technology | Artificial Intelligence | June 19, 2019
Zebra Medical Vision announced it has received its third U.S. Food and Drug Administration (FDA) 510(k) clearance for...
Overlay Init