Medical imaging and radiation science students' use of artificial intelligence for learning and assessment

Abstract

IntroductionArtificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography students' use of artificial intelligence for learning and assessment. Therefore, this study aimed to gain an understanding of this phenomenon. MethodsThe study used a qualitative explorative and descriptive research design. Data was obtained through five focus group interviews with purposively sampled undergraduate medical imaging and radiation science students at a single higher education institution in South Africa. Verbatim transcripts of the audio-recorded interviews were analysed thematically. ResultsThree themes and related subthemes were developed: 1) understanding artificial intelligence, 2) experiences with the use of artificial intelligence with the subthemes of the use of artificial intelligence in theoretical and clinical learning and challenges of using artificial intelligence, and 3) incorporation of artificial intelligence in undergraduate medical imaging and radiation sciences education with the subthemes of student education, ethical considerations and responsible use and curriculum integration of artificial intelligence in relation to learning and assessment. ConclusionParticipants used artificial intelligence for learning and assessment by generating ideas to enhance academic writing, as a learning tool, finding literature, language translation and for enhanced efficiency. Simulation-based artificial intelligence supports students' clinical learning, and artificial intelligence within the clinical departments assists with improved patient outcomes. However, participants expressed concerns about the reliability and ethical implications of artificial intelligence-generated information. To address these concerns, participants suggested integrating artificial intelligence into medical imaging and radiation sciences education, where educators need to educate students on the responsible use of artificial intelligence in learning and consider artificial intelligence in assessments. Implications for practiceThe study findings contribute to understanding medical imaging and radiation sciences students’ use of artificial intelligence and may be used to develop evidence-based strategies for integrating artificial intelligence into the curriculum to enhance medical imaging and radiation sciences education and support students.