Evidence map›Paper›PMID 38228800›Full record

ArticleInsights into imaging2024

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

Maria Jorina van Kooten, Can Ozan Tan, Elfi Inez Saïda Hofmeijer, Peter Martinus Adrianus van Ooijen, Walter Noordzij, Maria Jolanda Lamers, Thomas Christian Kwee, Rozemarijn Vliegenthart, Derya Yakar

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 pooled it
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Maria Jorina van KootenDepartment of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands. m.j.van.kooten@umcg.nl.ORCID http://orcid.org/0000-0002-1782-8803
Can Ozan TanRobotics and Mechatronics Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands.
Elfi Inez Saïda HofmeijerRobotics and Mechatronics Group, Faculty of Electrical Engineering, Mathematics, and Computer Science, University of Twente, PO Box 217, 7500 AE, Enschede, The Netherlands.
Peter Martinus Adrianus van OoijenDepartment of Radiation Oncology, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
Walter NoordzijDepartment of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
Maria Jolanda LamersDepartment of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
Thomas Christian KweeDepartment of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
Rozemarijn VliegenthartDepartment of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
Derya YakarDepartment of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
University Medical Center Groningen · NLUniversity of Twente · NL

Funding

ZonMw 104006003
6 · The paper itself

Abstract

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 STATEMENT: The 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: • AI education is necessary to prepare a new generation of AI-conscious radiologists. • The AI curriculum increased participants' perception of AI knowledge and skills in radiology. • This five-step framework can assist integrating AI education into radiology residency programs.

Indexed as

Artificial intelligenceCurriculumMedical informaticsResidencyTraining

Identifiers

PMID38228800
PMCPMC10792147
OpenAlexW4390928552

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.