A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist

被引:11
|
作者
van Kooten, Maria Jorina [1 ]
Tan, Can Ozan [2 ]
Hofmeijer, Elfi Inez Saida [2 ]
van Ooijen, Peter Martinus Adrianus [3 ,4 ]
Noordzij, Walter [5 ]
Lamers, Maria Jolanda [1 ]
Kwee, Thomas Christian [1 ]
Vliegenthart, Rozemarijn [1 ,4 ]
Yakar, Derya [1 ]
机构
[1] Univ Groningen, Univ Med Ctr Groningen, Med Imaging Ctr, Dept Radiol, POB 30001, NL-9700 RB Groningen, Netherlands
[2] Univ Twente, Robot & Mechatron Grp, Fac Elect Engn Math & Comp Sci, POB 217, NL-7500 AE Enschede, Netherlands
[3] Univ Groningen, Univ Med Ctr Groningen, Dept Radiat Oncol, POB 30001, NL-9700 RB Groningen, Netherlands
[4] Univ Groningen, Univ Med Ctr Groningen, Data Sci Ctr Hlth DASH, Machine Learning Lab, POB 30001, NL-9700 RB Groningen, Netherlands
[5] Univ Groningen, Univ Med Ctr Groningen, Med Imaging Ctr, Dept Nucl Med & Mol Imaging, POB 30001, NL-9700 RB Groningen, Netherlands
关键词
Artificial intelligence; Curriculum; Medical informatics; Training; Residency;
D O I
10.1186/s13244-023-01595-3
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
ObjectivesTo present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists.MethodsThe AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents' knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding. Pre- and post-curriculum surveys were conducted to assess participants' expectations and progress and were analyzed using a Wilcoxon rank-sum test.ResultsThere was 100% response rate to the pre- and post-curriculum survey (17 and 12 respondents, respectively). Participants' confidence in their knowledge and understanding of AI in radiology significantly increased after completing the program (pre-curriculum means 3.25 +/- 1.48 (SD), post-curriculum means 6.5 +/- 0.90 (SD), p-value = 0.002). A total of 75% confirmed that the course addressed topics that were applicable to their work in radiology. Lectures on the fundamentals of AI and group discussions with experts were deemed most useful.ConclusionDesigning an AI curriculum for radiology residents and implementing it into a radiology residency program is feasible using the framework presented. The 3-day AI curriculum effectively increased participants' perception of knowledge and skills about AI in radiology and can serve as a starting point for further customization.Critical relevance statementThe framework provides guidance for developing and implementing an AI curriculum in radiology residency programs, educating residents on the application of AI in radiology and ultimately contributing to future high-quality, safe, and effective patient care.Key points center dot AI education is necessary to prepare a new generation of AI-conscious radiologists.center dot The AI curriculum increased participants' perception of AI knowledge and skills in radiology.center dot This five-step framework can assist integrating AI education into radiology residency programs.Key points center dot AI education is necessary to prepare a new generation of AI-conscious radiologists.center dot The AI curriculum increased participants' perception of AI knowledge and skills in radiology.center dot This five-step framework can assist integrating AI education into radiology residency programs.Key points center dot AI education is necessary to prepare a new generation of AI-conscious radiologists.center dot The AI curriculum increased participants' perception of AI knowledge and skills in radiology.center dot This five-step framework can assist integrating AI education into radiology residency programs.
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页数:14
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