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Evaluation of a classroom-based medical imaging artificial intelligence educational intervention in Ghana: A
A Donkor1, E Boakye2, P Atuanor2
1Department of Medical Imaging, Faculty of Allied Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; IMPACCT (Improving Palliative, Aged and Chronic Care through Clinical Research and Translation), Faculty of Health, University of Technology Sydney, Australia.
Introduction:
The adoption of artificial intelligence (AI) is gaining increased interest in medical imaging. However, most medical imaging students in Ghana do not receive training on AI as part of their education. This study aimed to evaluate the effect of a tailored classroom-based medical imaging AI educational intervention in Ghana.
Methods:
A pre-test/post-test study was conducted. Medical imaging students were recruited. A one-week structured lecture format was employed, integrating pre-tests at the beginning of each class, followed by theoretical presentations, discussions and post-tests. The pre-test and post-test questions were identical to assess retention and attention. The pre-test survey consisted of socio-demographic details, basic medical imaging AI concepts, applications of AI, developing AI systems and AI ethics. Descriptive, paired t-tests and multiple linear regression analyses were performed.
Results:
A total of 144 medical imaging students participated in this study, with a mean age of 21 ± 2.41 years. All the participants indicated that they have not received any previous training on medical imaging AI systems. There were significant improvements in participants' knowledge and understanding on basic concepts in medical imaging AI, applications of AI in medical imaging, developing medical imaging AI systems and AI ethics after the intervention (p < 0.001). Year of study was identified as a predictive factor to increased understanding post-test (p = 0.015).
Conclusion:
The results of this study showed strong evidence that classroom-based intervention is an effective approach to improving students' knowledge and understanding on medical imaging AI systems.
Implication For Practice:
This short medical imaging AI course can be integrated into the medical imaging curriculum in Ghana to provide students with theoretical knowledge in AI.
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