In a survey of 62 radiology training programs in the U.S., a recent study found that less than half had any formal artificial intelligence (AI) educational initiatives and only 3% of programs advertised their training pathway to residents or fellows [1]. AI education and hands-on experience with AI have been shown to mitigate fear of AI. Perhaps more importantly, radiology trainees who have experience with AI are able to develop more realistic expectations of what AI can and cannot do. Radiology trainees often report that AI is important to their medical training, however few radiology trainees feel that they have had enough AI exposure through their training program.

Opportunities for AI education are becoming more available for radiology trainees, with more radiology residencies instituting AI training curricula, radiological societies hosting online courses, in addition to the variety of free courses on the basics of coding and machine learning available online [2,3]. The field of radiology has recognized the importance of AI education in radiology and is slowly taking steps to address this need, however substantial barriers to accessible and practical AI education remain. The disparity in the number of programs with AI education opportunities and the number of radiology trainees is stark, and this disparity becomes even more pronounced in under-resourced healthcare settings and outside the United States.

The AI Literacy Course, hosted by Artificial Intelligence in Radiology Education (AIRE), is the largest free Radiology AI education resource in the world, reaching 500 participants at 25 US programs and in 10 countries last year. This course serves to provide accessible, fundamental, and practical AI education and to provide the tools for radiologists to thrive in the future of radiology.

References

1. Li D, Morkos J, Gage D, Yi PH. Artificial intelligence education and research initiatives and leadership positions in academic radiology departments. Current Problems in Diagnostic Radiology. 2022; 51(4): 552-555.
2. Lindqwister AL, Hassanpour S, Lewis PJ, Sin JM. AI-RADS: An artificial intelligence curriculum for residents. Academic Radiology. 2021. 28(12): 1810-1816. https://doi.org/10.1016/j.acra.2020.09.017
3. RSNA AI Certificate Program. Radiological Society of North America. https://www.rsna.org/ai-certificate. Accessed 11 April 2022.