AI-RADS: an artificial intelligence curriculum for residents
Open accessAbstract
Rationale and objectives Artificial intelligence (AI) has rapidly emerged as a field poised to affect nearly every aspect of medicine, especially radiology. A PubMed search for the terms "artificial intelligence radiology" demonstrates an exponential increase in publications on this topic in recent years. Despite these impending changes, medical education designed for future radiologists have only recently begun. We present our institution's efforts to address this problem as a model for a successful introductory curriculum into artificial intelligence in radiology titled AI-RADS.Materials and methods The course was based on a sequence of foundational algorithms in AI; these algorithms were presented as logical extensions of each other and were introduced as familiar examples (spam filters, movie recommendations, etc.). Since most trainees enter residency without computational backgrounds, secondary lessons, such as pixel mathematics, were integrated in this progression. Didactic sessions were reinforced with a concurrent journal club highlighting the algorithm discussed in the previous lecture. To circumvent often intimidating technical descriptions, study guides for these papers were produced. Questionnaires were administered before and after each lecture to assess confidence in the material. Surveys were also submitted at each journal club assessing learner preparedness and appropriateness of the article.Results The course received a 9.8/10 rating from residents for overall satisfaction. With the exception of the final lecture, there were significant increases in learner confidence in reading journal articles on AI after each lecture. Residents demonstrated significant increases in perceived understanding of foundational concepts in artificial intelligence across all mastery questions for every lecture.Conclusion The success of our institution's pilot AI-RADS course demonstrates a workable model of including AI in resident education.
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Lindqwister et al. presented an AI curriculum for radiology residents with didactic sessions and journal clubs, aligning with our elements on AI strengths and limitations (T9) and regulatory issues (E1).
“Lindqwister et al. presented an AI curriculum for radiology residents with didactic sessions and journal clubs, aligning with our elements on AI strengths and limitations (T9) and regulatory issues (E1) to ensure a balanced technical and ethical education19.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [19]
The Introduction describes AI-RADS as a curriculum for radiology residents intended to teach the strengths and limitations of machine-learning techniques. The Abstract’s Materials and Methods and Methods > Journal Club describe didactic sessions reinforced by journal clubs. Our copy does not show that the curriculum covered regulatory issues or ethical education, so the claimed alignment with E1 is unsupported. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSeveral groups have investigated implementing ad-hoc extracurricular programs to improve AI literacy.
“Several groups have investigated implementing ad-hoc extracurricular programs to improve AI literacy, such as the workshops led by Hu et al. and the Artificial Intelligence Curriculum for Residents (AI-RADS) program at Dartmouth [10,11].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [11]
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- Authors
- Lindqwister, A. L., Hassanpour, S., Lewis, P. J., & Sin, J. M.
- Affiliations
- Dartmouth College (US)
- Dartmouth–Hitchcock Medical Center (US)
- Corresponding author
- Lindqwister, Alexander L.
- Venue
- Academic Radiology
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 28
- Issue
- 12
- Pages
- 1810-1816
- ISSN
- 1076-6332 (ISSN-L)
- PMC
- PMC7563580
- OpenAlex
- W3093404209
- Cited by
- 103
- References
- 16
- Citations by year
- 2026: 15 · 2025: 16 · 2024: 23 · 2023: 15 · 2022: 24 · 2021: 10
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Academic Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (P30CA023108)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- artificial intelligence in radiology, radiology residency education, AI curriculum, journal club, learner confidence, computational literacy, medical education
- MeSH terms
- Radiologists, Artificial Intelligence, Curriculum, Humans, Internship and Residency, Radiology
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