Evidence map›Paper›PMID 36397110›Full record

ArticleBMC medical education2022

Effect of a flipped classroom course to foster medical students' AI literacy with a focus on medical imaging: a single group pre-and post-test study.

Matthias C Laupichler, Dariusch R Hadizadeh, Maximilian W M Wintergerst, Leon von der Emde, Daniel Paech, Elizabeth A Dick, Tobias Raupach

Open access · goldAbstract read
In one paragraph

Article in BMC medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
2.8field-weighted citation impact, top 9% 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 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

7 authors at 2 institutions in 2 countries.

Matthias C LaupichlerInstitute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany. matthias.laupichler@ukbonn.de.
Dariusch R HadizadehClinic for Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
Maximilian W M WintergerstDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Leon von der EmdeDepartment of Ophthalmology, University Hospital Bonn, Bonn, Germany.
Daniel PaechClinic for Neuroradiology, University Hospital Bonn, Bonn, Germany.
Elizabeth A DickImperial College NHS Trust and Imperial College London, St. Marys Hospital London, London, UK.
Tobias RaupachInstitute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
University Hospital Bonn · DEImperial College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of artificial intelligence applications in medicine is becoming increasingly common. At the same time, however, there are few initiatives to teach this important and timely topic to medical students. One reason for this is the predetermined medical curriculum, which leaves very little room for new topics that were not included before. We present a flipped classroom course designed to give undergraduate medical students an elaborated first impression of AI and to increase their "AI readiness".

methodsThe course was tested and evaluated at Bonn Medical School in Germany with medical students in semester three or higher and consisted of a mixture of online self-study units and online classroom lessons. While the online content provided the theoretical underpinnings and demonstrated different perspectives on AI in medical imaging, the classroom sessions offered deeper insight into how "human" diagnostic decision-making differs from AI diagnoses. This was achieved through interactive exercises in which students first diagnosed medical image data themselves and then compared their results with the AI diagnoses. We adapted the "Medical Artificial Intelligence Scale for Medical Students" to evaluate differences in "AI readiness" before and after taking part in the course. These differences were measured by calculating the so called "comparative self-assessment gain" (CSA gain) which enables a valid and reliable representation of changes in behaviour, attitudes, or knowledge.

resultsWe found a statistically significant increase in perceived AI readiness. While values of CSA gain were different across items and factors, the overall CSA gain regarding AI readiness was satisfactory.

conclusionAttending a course developed to increase knowledge about AI in medical imaging can increase self-perceived AI readiness in medical students.

Indexed as

Students, MedicalArtificial IntelligenceCurriculumHumansLiteracySchools, MedicalAI literacyAI readinessArtificial intelligenceFlipped classroom

Identifiers

PMID36397110
PMCPMC9672614
OpenAlexW4309265012

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.