期刊
ACADEMIC RADIOLOGY
卷 30, 期 2, 页码 349-358出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.acra.2022.04.026
关键词
Medical education; Radiology curriculum; Artificial intelligence; Radiology education
This study aims to assess the needs for the development of an AI curriculum during pediatric radiology training and continuing education. A focus group study was conducted to understand the perceptions, competence, and expectations of radiology trainees and attending radiologists regarding AI. The results revealed heterogeneity in perspectives, with variations in AI knowledge, previous training, learning preferences, expectations, and concerns. The participants expressed a preference for case-based teaching and highlighted the need for improved training in interpreting and applying AI literature.
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 vari-ability 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 sce-narios could engage a variety of trainees from different backgrounds and interest levels while ensuring a baseline level of competency in AI.
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