The importance of AI knowledge is rapidly increasing for today’s radiologists. As the intersection of AI and medical practice becomes more integral, there is a heightened emphasis on embedding these technological advancements into the educational framework for physicians, especially within radiology residency programs. This paper presents a structured framework for the development and integration of a comprehensive AI curriculum into existing radiology residency programs. The curriculum, designed by a coalition of AI specialists, radiologists, and residents, includes didactic lectures, hands-on laboratory sessions, and in-depth discussions with AI professionals. It is worth noting that the program has significantly boosted participants’ confidence in their AI knowledge, marking a substantial advance from their foundational understanding before the curriculum’s implementation.
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Abstract
Objectives
To present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists.
Methods
The AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents’ knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants’ expectations and progress and were analyzed using a Wilcoxon rank-sum test.
Results
There was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants’ confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 ± 1.48 (SD), post-curriculum means 6.5 ± 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful.
Conclusion
Designing an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants’ perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization.
Critical relevance statement
The framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care.