RSNA Seeks Radiology: Artificial Intelligence Trainee Editorial Board Members

Apply by Oct. 1 for unique opportunity to serve on a journal editorial board

Radiology: Artificial Intelligence, RSNA’s online journal highlighting the emerging applications of machine learning and artificial intelligence (AI) in the field of imaging, is seeking applications from radiology residents, radiology fellows, graduate students and postdocs for positions on the journal’s Trainee Editorial Board (TEB).


During their two-year term, TEB members will learn about peer review, biostatistics, research design and journalistic ethics, while reviewing up to six manuscripts each year.  TEB members will also participate in the standard Radiology: Artificial Intelligence review process and have the opportunity to contribute to the journal’s activities, such as the editor’s blog, social media, or journal club. Members will also be invited to participate in the Editorial Board meeting during the RSNA Annual Meeting and in quarterly teleconferences with the Radiology: Artificial Intelligence Editor, Charles E. Kahn Jr., MD, MS.


Applicants must be in training in a relevant residency, fellowship, graduate degree program, or postdoctoral fellowship throughout their TEB term.  Applicants must be in one or more of these eligible medical disciplines: diagnostic radiology, interventional radiology, nuclear medicine and radiation oncology; or in one or more of these eligible scientific disciplines: medical physics, biomedical informatics, biomedical engineering and computer science. Applicants should have strong critical-thinking skills, a mastery of written English and experience in scientific writing.


To apply, applicants should submit a:

  • Two to four paragraph statement of interest;
  • Copy of their CV;
  • Copy of one publication representative of their work; and
  • Statement from an onsite faculty member at their program who is willing to serve as their mentor during their time on the TEB.

Applicants must be RSNA members currently and throughout their term. Candidates should send questions and/or applications materials to by Oct. 1.


For More Information

Access the current issue of Radiology: Artificial Intelligence.