A recent survey evaluated radiology residents’ perspectives regarding the inclusion of artificial intelligence education in the curriculum.
The survey was delivered to 759 residents at 21 U.S. radiology residency programs. Around 80% of respondents had little prior involvement in artificial intelligence (AI), or machine learning (ML) research, and 51% had little experience in using an AI or ML tool for work or research.
Over 80% of radiology residents expressed their agreement that AI and machine learning should be included in their curriculum; 76% of residents felt a continuous course on these subjects across their four years of residency should be included.
When questioned about their preferred method of education on AI and ML, many of the respondents expressed their preference for hands-on and laboratory training; approximately 67% of residents stressed hands on AI/ML laboratory was the most effective, whilst 61% of residents felt that lecture series was the most effective; 41% preferred institutional courses and online videos. 82% of residents feel that such education should provide them with the knowledge to troubleshoot an AI tool and enhance their assessment of whether a tool is working in practice. 12% desired to learn how to use an AI tool without having to learn troubleshooting.
Lars J. Grimm, co-author and professor of radiology at Duke University, said, “These data demonstrate the need for improving awareness of existing AI education resources through better marketing/education as well as (identifying) opportunities for developing courses/curricula according to the preferred modes of learning reported by the residents.”
Overall, the study highlighted that residents are in favour of including AI/ML education in the curriculum, particularly hands-on learning and lectures.
Source: Academic Radiology
Image Credit: iStock
Salasterkar V N et al.(2023) Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States. Academic Radiology