Objective: Although educating radiology trainees about artificial intelligence (AI) has become increasingly emphasized, the types of AI educational curricula are not well understood. We performed a systematic review of original studies describing curricula used to teach AI concepts and practical applications for radiology residents and fellows.
Materials and methods: We performed a PubMed search for original studies published as of July 22, 2022, describing AI curricula geared toward radiology residents or fellows. Studies meeting inclusion criteria were evaluated for curricula design, implementation details, and outcomes. Descriptive statistics were used to summarize these curricula.
Results: Five studies were included describing an AI curriculum, all geared toward radiology residents. All five curricula were led by radiologists, mostly by individual academic radiology departments (4; 80%) with one led by the ACR Resident and Fellow Section. Curricula design included didactic sessions (5; 100%), assigned readings (4; 80%), hands-on learning (3; 60%), and journal clubs (3; 60%); only one had individualized learning plans. All four studies that evaluated the impact of the curricula on participants' knowledge or attitudes showed positive effects.
Discussion: Amid increasing recognition of the importance of AI education for radiologists-in-training, several AI curricula for radiology residents have been implemented. Although curricula designs varied and it is unclear if one type is superior, they have had a positive impact on residents' knowledge and attitudes toward AI. As AI becomes increasingly adopted in radiology, these curricula serve as models for other departments and programs to develop AI educational initiatives to prepare the next generation of radiologists for the AI era.
Keywords: Artificial intelligence; curricula; radiology; residency; training.
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