Rationale and Objectives
Artificial intelligence (AI) holds enormous potential for improvements in patient
care, efficiency, and innovation in pediatric radiology practice. Although there is
a pressing need for a radiology-specific training curriculum and formalized AI teaching,
few resources are available. The purpose of our study was to perform a needs assessment
for the development of an AI curriculum during pediatric radiology training and continuing
education.
Materials and Methods
A focus group study using a semistructured moderator-guided interview was conducted
with radiology trainees’ and attending radiologists’ perceptions of AI, perceived
competence in interpretation of AI literature, and perceived expectations from radiology
AI educational programs. The focus group was audio-recorded, transcribed, and thematic
analysis was performed.
Results
The focus group was held virtually with seven participants. The following themes we
identified: (1) AI knowledge, (2) previous training, (3) learning preferences, (4)
AI expectations, and (5) AI concerns. The participants had no previous formal training
in AI and variability in perceived needs and interests. Most preferred a case-based
approach to teaching AI. They expressed incomplete understanding of AI hindered its
clinical applicability and reiterated a need for improved training in the interpretation
and application of AI literature in their practice.
Conclusion
We found heterogeneity in perspectives about AI; thus, a curriculum must account for
the wide range of these interests and needs. Teaching the interpretation of AI research
methods, literature critique, and quality control through implementation of specific
scenarios could engage a variety of trainees from different backgrounds and interest
levels while ensuring a baseline level of competency in AI.
Keywords
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Article Info
Publication History
Published online: June 23, 2022
Accepted:
April 30,
2022
Received in revised form:
April 16,
2022
Received:
March 4,
2022
Publication stage
In Press Corrected ProofFootnotes
The author(s) declare(s) that they had full access to all the data in this study and the author(s) take(s) complete responsibility for the integrity of the data and the accuracy of the data analysis.
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
