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.
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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Assessing the effectiveness of artificial intelligence education and training for healthcare workers: a systematic review.BMC medical education · 2026Pooled it
- Integrating generative AI into physiotherapy education: students' use, perceptions, and generative AI literacy following curricular adaptations: a repeated cross-sectional programme evaluation.BMC medical education · 2026Article
- A Competency Framework for Medical AI Education: Mixed Methods Study.JMIR medical education · 2026Article
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- Framework for AI integration into the undergraduate medical curricula: a scoping review.BMC medical education · 2026Article
- What and when: a Delphi study identifying AI competencies and learning objectives for medical students.Frontiers in digital health · 2026Article
- Current Landscape of Curriculum Development and Implementation in Medical Artificial Intelligence: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Awareness, trust, and expectations of AI for glaucoma care among Bulgarian ophthalmologists: Role of demographic factors.PLOS digital health · 2026Article
- Exploring the integration of AI and national quality courses in China: a study on teaching practices in nursing smart education.BMC medical education · 2025Article
- Article
- Assessing medical students' readiness for artificial intelligence after pre-clinical training.BMC medical education · 2025Article
- AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study.JMIR medical education · 2025Article
- Development and assessment of a novel multimedia-based educational software for teaching peripheral blood smear morphology.BMC medical education · 2025Article
- Review
- Implementing spiritual care education into the teaching of palliative medicine: an outcome evaluation.BMC medical education · 2024Article
- Medical students' AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments.BMC medical education · 2024Article
- Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students.BMC medical education · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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