The Impact of an Artificial Intelligence Certificate Program on Radiology Resident Education.

Autor:	Finkelstein M; Icahn School of Medicine at Mount Sinai, New York, NY (M.F., A.K., K.P.H., D.S.M.); New York University Langone Medical Center (M.F.). Electronic address: mark.finkelstein@mountsinai.org., Ludwig K; Eastern Virginia Medical School (K.L.)., Kamath A; Icahn School of Medicine at Mount Sinai, New York, NY (M.F., A.K., K.P.H., D.S.M.)., Halton KP; Icahn School of Medicine at Mount Sinai, New York, NY (M.F., A.K., K.P.H., D.S.M.)., Mendelson DS; Icahn School of Medicine at Mount Sinai, New York, NY (M.F., A.K., K.P.H., D.S.M.).
Jazyk:	angličtina
Zdroj:	Academic radiology [Acad Radiol] 2024 Nov; Vol. 31 (11), pp. 4709-4714. Date of Electronic Publication: 2024 Jun 21.
DOI:	10.1016/j.acra.2024.05.041
Abstrakt:	Rationale and Objectives: The objective of this study was to evaluate the effectiveness of a pilot artificial intelligence (AI) certificate program in aiding radiology trainees to develop an understanding of the evolving role and application of artificial intelligence in radiology. A secondary objective was set to determine the background of residents that would most benefit from such training. Materials and Methods: This was a prospective pilot study involving 42 radiology residents at two separate residency programs who participated in the Radiological Society of North America Imaging AI Foundational Certificate course over a four-month period. The course consisted of 6 online modules that contained didactic lectures followed by end-of-module quizzes to assess knowledge gained from these lectures. Pre- and post-course assessments were conducted to evaluate the residents' knowledge and skills in AI. Additionally, a post-course survey was performed to assess participants' overall satisfaction with the course. Results: All participating residents completed the certificate program. The mean pre-course assessment score was 37 %, which increased to 73 % after completing the modules (p < 0.001). 74 % (31/42) endorsed the belief the course improved familiarity with artificial intelligence in radiology. Residency program, residency year, and reported prior familiarity with AI were not found to influence pre-course score, post-course score, nor score improvement. 57 % (24/42) endorsed interest in pursuing further certification in AI. Conclusion: Our pilot study suggests that a certificate course can effectively enhance the knowledge and skills of radiology residents in the application of AI in radiology. The benefits of such a course can be found regardless of program, resident year, and self-reported prior resident understanding of radiology in AI. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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