Lectures on AI in Healthcare, an Interdisciplinary Learning Approach

Abstract

Teaching students about Artificial Intelligence (AI) in healthcare presents significant challenges for both educators and students. This study examines the lecture AI in Healthcare that was conducted at the Niederrhein University of Applied Sciences as part of the master's programme Health Care. The lecture is intended for students from both the Faculty of Health Care and the Faculty of Computer Sciences. It aims to provide them with an understanding of the fundamental concepts of AI and the practical usage in healthcare through exercises and interdisciplinary group work. Despite the positive feedback on the relevance of AI and ML, students identified several challenges. These challenges include the lack of fundamental knowledge about healthcare specific datasets, fundamental AI concepts, and also difficulties in the context of the interdisciplinary collaboration. These issues were provided by the implementation of a Teaching Analysis Poll (TAP) halfway through the lecture. This study shows the necessity for preparatory courses and the provision of resources. This could bridge knowledge gaps and enhance the practical relevance of the lecture within the curriculum. Additionally, the use of visual programming tools like Orange Data Mining proved beneficial, especially for nontechnical students. The findings suggest, that by addressing these gaps and refining instructional methods, the learning experience can be improved and the students can be better prepared for the complexities of the healthcare industry.

Ähnliche Arbeiten