Gamification for emergency radiology education and image perception: stab the diagnosis
Year: 2025
Type: article
Abstract: PurposeGamification can be a helpful adjunct to education and is increasingly used in radiology. We aim to determine if using a gamified framework to teach medical trainees about emergency radiology can improve perceptual and interpretive skills and facilitate learning.ApproachWe obtained approval from the Institutional Review Board, and participation was voluntary. Participants received training at the RadSimPE radiology workstation simulator and were shown three sets of computed tomography images related to emergency radiology diagnoses. Participants were asked to state their certainty that an abnormality was not present, localize it if present, and give their confidence in localization. Between case sets 1 and 2, the experimental group was provided with gamified emergency radiology training on the Stab the Diagnosis program, whereas the control group was not. Following the session, participants completed an eight-question survey to assess their thoughts about the training.ResultsA total of 36 medical trainees participated. Both the experimental group and control group improved in localization accuracy, but the experimental group’s localization confidence was significantly greater than the control group (p=0.0364). Survey results were generally positive and were statistically significantly greater than the neutral value of 3, with p-values <0.05 for all eight questions. For example, survey results indicated that participants felt the training was a helpful educational experience (p<0.001) and that the session was more effective for learning than traditional educational techniques (p=0.001).ConclusionsGamification may be a valuable adjunct to conventional methods in radiology education and may improve trainee confidence.
Source: Journal of medical imaging
Authors: William F. Auffermann, Nathan Barber, Ryan Stockard, Soham Banerjee
Institutions: University of Utah, Baylor College of Medicine
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4414479030
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