Artificial Intelligence/Machine Learning Education in Radiology: Multi-institutional Survey of Radiology Residents in the United States.

RATIONALE AND OBJECTIVES: To evaluate radiology residents' perspectives regarding inclusion of artificial intelligence/ machine learning (AI/ML) education in the residency curriculum. MATERIALS AND METHODS: An online anonymous survey was sent to 759 residents at 21 US radiology residency programs. Resident demographics, sub-specialty interests, educational background and research experiences, as well as the awareness, availability, and usefulness of various resources for AI/ML education were collected. RESULTS: The survey response rate was 27% (209/759). A total of 74% of respondents were male, 80% were training at large university programs, and only a minority (<20) had formal education or research experience in AI/ML. All four years of training were represented (range: 20%-38%). The majority of the residents agreed or strongly agreed (83%) that AI/ML education should be a part of the radiology residency curriculum and that such education should equip them with the knowledge to troubleshoot an AI tool in practice / determine whether a tool is working as intended (82%). Among the residency programs that offer AI/ML education, the most common resources were lecture series (43%), national informatics courses (28%), and in-house/institutional courses (26%). About 24% of the residents reported no AI/ML educational offerings in their residency curriculum. Hands on AI/ML laboratory (67%) and lecture series (61%) were reported as the most beneficial or effective. The majority of the residents preferred AI/ML education offered as a continuous course spanning the radiology residency (R1 to R4) (76%), followed by mini fellowship during R4 (32%) and as a course during PGY1 (21%). CONCLUSION: Residents largely favor the inclusion of formal AI/ML education in the radiology residency curriculum, prefer hands-on learning and lectures as learning tools, and prefer a continuous AI/ML course spanning R1-R4.

Duke Scholars

Author Charles Mason Maxfield Radiology, Pediatric Radiology

Author Lars Johannes L Grimm Radiology, Breast Imaging