Overview

Schema Version

https://atlas.rsna.org/schemas/2025-11/model.json

Name

An Artificial Intelligence Training Workshop for Diagnostic Radiology Residents

Link

https://dx.doi.org/10.1148/ryai.220170

Indexing

Keywords: Medical Education, Machine Learning, Postgraduate Training, Competency-based Medical Education, Medical Informatics

Content: ED, IN

RadLex: RID1, RID2

Author(s)

Ricky Hu

Arsalan Rizwan

Zoe Hu

Toni Li

Andrew David Chung

Benjamin Y.M. Kwan

Organization(s)

Queen’s University School of Medicine

Department of Diagnostic Radiology, Queen’s University

Version

1.0

License

URL: https://pubs.rsna.org/doi/10.1148/ryai.220170

Contact

R.H. (email: ac.demq@uhr)

Funding

Supported by the Department of Radiology Research Grant at Queen’s University.

Ethical review

Approved by the Queen’s University Health Sciences & Affiliated Teaching Hospitals Research Ethics Board (code: RADO-107-21).

Date

Updated: 2023-03-01

Published: 2023-02-08

Created: 2022-08-14

Model

Availability

Curricular content and programming examples available at https://github.com/Queens-Radiology-Intro-To-AI/Intro-to-AI

Clinical workflow phase

Education/training within residency academic half-days

Indications for use

Introductory AI literacy workshop for diagnostic radiology residents within a competency-based medical education residency program.

Instructions

Workshop delivered over three 1-hour sessions with didactic lectures, case studies, and programming demonstrations; optional 1-hour self-study materials after weeks 1–2; delivered virtually during academic half-days.

Limitations

Single-institution pilot with small sample size; attendance not mandatory (potential selection bias); knowledge retention not assessed with formal examinations; focus on foundational literacy rather than technical proficiency.

Output

CDEs: RDE2886, RDE2885

Use

Intended: Other

User

Intended: Other